# data analysis and wrangling
import pandas as pd
import numpy as np
import random as rnd
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
pd.set_option('display.max_columns', 50)
sns.set(style='darkgrid')
sns.set_palette('husl', 8)
plt.rcParams['font.family'] = 'Times New Roman'
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
filepath = r'~/Desktop/Desktop/Prasmul/Semester 6/Applied Data Science for Business/Project Group Dataset/'
filename = r'cctx_train.xlsx'
data = pd.read_excel(filepath + filename)
data.head()
| Transaction_ID | Transaction_Flag | Transaction_Date | Transaction_Type | Transaction_Amount | Bank_ID | CC_ID | Card_Type | Card_Holder | Channel_ID | Merchant_ID | Country_ID | City_ID | EDC_Type | EDC_Location | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00002 | False | 2018-01-01 01:48:50.951 | T08 | 50000.0 | 1 | CCID5563 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC0885 | LEDC3703 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | TX00004 | False | 2018-01-01 09:08:52.666 | T01 | 1000000.0 | 1 | CCID4598 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-129 | EDC0565 | LEDC3205 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | TX00005 | False | 2018-01-01 09:08:52.666 | T15 | 1000000.0 | 1 | CCID2839 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-073 | EDC4639 | LEDC3081 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | TX00006 | False | 2018-01-01 09:45:55.969 | T15 | 1000000.0 | 1 | CCID2968 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-129 | EDC3918 | LEDC1993 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | TX00007 | False | 2018-01-01 23:41:59.228 | T10 | 2500000.0 | 1 | CCID0176 | CC12 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC2863 | LEDC2062 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
Check data info to see data type of each variables.
data.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10500 entries, 0 to 10499 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transaction_ID 10500 non-null object 1 Transaction_Flag 10500 non-null bool 2 Transaction_Date 10500 non-null datetime64[ns] 3 Transaction_Type 10500 non-null object 4 Transaction_Amount 10500 non-null float64 5 Bank_ID 10500 non-null int64 6 CC_ID 10500 non-null object 7 Card_Type 10500 non-null object 8 Card_Holder 10500 non-null int64 9 Channel_ID 10500 non-null int64 10 Merchant_ID 10500 non-null object 11 Country_ID 10500 non-null object 12 City_ID 10500 non-null object 13 EDC_Type 10500 non-null object 14 EDC_Location 10500 non-null object 15 EDC_Owner 10500 non-null object 16 Average_Transaction_Amount 10484 non-null float64 17 Maximum_Transaction_Amount 10484 non-null float64 18 Minimum_Transaction_Amount 10484 non-null float64 19 Average_Transaction_Frequency 10484 non-null float64 20 Fraud_Status 10500 non-null int64 dtypes: bool(1), datetime64[ns](1), float64(5), int64(4), object(10) memory usage: 7.2 MB
Check whether any of the variables have null data inside.
def null_counts(df, style=True):
nulls = df.isna().sum().rename_axis('Columns').reset_index(name='Count')
nulls['Percentage'] = nulls['Count'] / len(df)
nulls = nulls.loc[nulls['Count'] > 0]
nulls.sort_values(by='Count', ascending=False, inplace=True)
nulls.reset_index(drop=True, inplace=True)
if style:
nulls = nulls.style.format({'Count': '{:,}', 'Percentage': '{:.2%}'}).hide_index()
return nulls
null_counts(data)
| Columns | Count | Percentage |
|---|---|---|
| Average_Transaction_Amount | 16 | 0.15% |
| Maximum_Transaction_Amount | 16 | 0.15% |
| Minimum_Transaction_Amount | 16 | 0.15% |
| Average_Transaction_Frequency | 16 | 0.15% |
As variables Average_Transaction_Amount, Maximum_Transaction_Amount, Minimum_Transaction_Amount, Average_Transaction_Frequency all have 16 nulls. We drop all rows that contain these null data.
data.dropna()
| Transaction_ID | Transaction_Flag | Transaction_Date | Transaction_Type | Transaction_Amount | Bank_ID | CC_ID | Card_Type | Card_Holder | Channel_ID | Merchant_ID | Country_ID | City_ID | EDC_Type | EDC_Location | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00002 | False | 2018-01-01 01:48:50.951 | T08 | 50000.0 | 1 | CCID5563 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC0885 | LEDC3703 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | TX00004 | False | 2018-01-01 09:08:52.666 | T01 | 1000000.0 | 1 | CCID4598 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-129 | EDC0565 | LEDC3205 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | TX00005 | False | 2018-01-01 09:08:52.666 | T15 | 1000000.0 | 1 | CCID2839 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-073 | EDC4639 | LEDC3081 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | TX00006 | False | 2018-01-01 09:45:55.969 | T15 | 1000000.0 | 1 | CCID2968 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-129 | EDC3918 | LEDC1993 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | TX00007 | False | 2018-01-01 23:41:59.228 | T10 | 2500000.0 | 1 | CCID0176 | CC12 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC2863 | LEDC2062 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | TX13121 | False | 2018-12-31 15:47:34.782 | T13 | 5000000.0 | 1 | CCID4018 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-023 | EDC0741 | LEDC3813 | OEDC0377 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | TX13122 | False | 2018-12-31 15:47:34.782 | T13 | 4800000.0 | 1 | CCID7476 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-181 | EDC0797 | LEDC3826 | OEDC0377 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | TX13123 | False | 2018-12-31 15:47:34.782 | T13 | 2500000.0 | 1 | CCID7625 | CC00 | 2 | 1 | M0001 | CTY06 | CTY06-023 | EDC0587 | LEDC3795 | OEDC0377 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | TX13124 | False | 2018-12-31 15:47:34.782 | T13 | 1100000.0 | 1 | CCID6508 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-186 | EDC0626 | LEDC3811 | OEDC0377 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | TX13125 | False | 2018-12-31 22:11:05.961 | T02 | 102500.0 | 1 | CCID5211 | CC11 | 2 | 2 | M0597 | CTY06 | CTY06-171 | EDC4477 | LEDC4583 | OEDC0633 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10484 rows × 21 columns
data.describe()
| Transaction_Amount | Bank_ID | Card_Holder | Channel_ID | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|
| count | 1.050000e+04 | 10500.0 | 10500.000000 | 10500.000000 | 1.048400e+04 | 1.048400e+04 | 1.048400e+04 | 10484.000000 | 10500.000000 |
| mean | 1.320409e+06 | 1.0 | 1.932190 | 1.405333 | 1.366425e+06 | 1.229132e+07 | 7.735985e+04 | 2.435969 | 0.069333 |
| std | 2.819677e+06 | 0.0 | 0.251431 | 1.013994 | 1.439952e+06 | 1.640702e+07 | 7.496893e+05 | 1.390293 | 0.254032 |
| min | 1.000000e+00 | 1.0 | 1.000000 | 1.000000 | 5.000000e+04 | 3.800000e+04 | 1.000000e+00 | 1.000000 | 0.000000 |
| 25% | 2.000000e+05 | 1.0 | 2.000000 | 1.000000 | 5.708772e+05 | 2.500000e+06 | 2.500000e+04 | 1.680000 | 0.000000 |
| 50% | 5.800000e+05 | 1.0 | 2.000000 | 1.000000 | 1.026553e+06 | 6.000000e+06 | 3.750000e+04 | 2.100000 | 0.000000 |
| 75% | 1.250000e+06 | 1.0 | 2.000000 | 1.000000 | 1.691944e+06 | 1.500000e+07 | 6.550000e+04 | 2.780000 | 0.000000 |
| max | 6.000000e+07 | 1.0 | 2.000000 | 5.000000 | 2.466667e+07 | 1.000000e+08 | 7.500000e+07 | 19.780000 | 1.000000 |
data.describe(include=['O'])
| Transaction_ID | Transaction_Type | CC_ID | Card_Type | Merchant_ID | Country_ID | City_ID | EDC_Type | EDC_Location | EDC_Owner | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 10500 | 10500 | 10500 | 10500 | 10500 | 10500 | 10500 | 10500 | 10500 | 10500 |
| unique | 10500 | 20 | 7573 | 14 | 975 | 11 | 227 | 4789 | 5238 | 1461 |
| top | TX02634 | T01 | CCID2268 | CC11 | M0001 | CTY06 | CTY06-023 | EDC0002 | LEDC0400 | OEDC0377 |
| freq | 1 | 2872 | 5 | 3841 | 9037 | 10464 | 4255 | 707 | 16 | 8325 |
data = data.drop(['Transaction_ID','Transaction_Flag','Country_ID','Bank_ID','CC_ID','EDC_Location'], axis=1)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | T08 | 50000.0 | CC11 | 2 | 1 | M0001 | CTY06-133 | EDC0885 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | T01 | 1000000.0 | CC08 | 2 | 1 | M0001 | CTY06-129 | EDC0565 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | T15 | 1000000.0 | CC08 | 2 | 1 | M0001 | CTY06-073 | EDC4639 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | T15 | 1000000.0 | CC08 | 2 | 1 | M0001 | CTY06-129 | EDC3918 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | T10 | 2500000.0 | CC12 | 2 | 1 | M0001 | CTY06-133 | EDC2863 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
def value_counts(df, col, style=True):
table = df[col].value_counts().rename_axis('Value').reset_index(name='Count')
table['Percentage'] = table['Count'] / table['Count'].sum(axis=0)
if style:
table = table.style.format({'Count': '{:,}', 'Percentage': '{:.2%}'}).hide_index()
return table
value_counts(data, 'Fraud_Status')
| Value | Count | Percentage |
|---|---|---|
| 0 | 9,772 | 93.07% |
| 1 | 728 | 6.93% |
labels=['Not Fraud','Fraud']
colors=['green', 'red']
data['Fraud_Status'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.1f%%', labels=labels, colors=colors, radius=1.5)
<AxesSubplot:ylabel='Fraud_Status'>
value_counts(data, 'Card_Type')
| Value | Count | Percentage |
|---|---|---|
| CC11 | 3,841 | 36.58% |
| CC08 | 2,113 | 20.12% |
| CC09 | 1,381 | 13.15% |
| CC00 | 1,260 | 12.00% |
| CC10 | 1,093 | 10.41% |
| CC04 | 237 | 2.26% |
| CC02 | 157 | 1.50% |
| CC01 | 155 | 1.48% |
| CC05 | 145 | 1.38% |
| CC12 | 54 | 0.51% |
| CC13 | 36 | 0.34% |
| CC03 | 20 | 0.19% |
| CC06 | 7 | 0.07% |
| CC07 | 1 | 0.01% |
data['Card_Type'].replace({'CC11': 0, 'CC08': 1, 'CC09':2, 'CC00':3, 'CC10': 4}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | T08 | 50000.0 | 0 | 2 | 1 | M0001 | CTY06-133 | EDC0885 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | T01 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC0565 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | T15 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-073 | EDC4639 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | T15 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC3918 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | T10 | 2500000.0 | CC12 | 2 | 1 | M0001 | CTY06-133 | EDC2863 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
unused = data['Card_Type'].loc[(data['Card_Type']!= 0)&(data['Card_Type']!=1)& (data['Card_Type']!=2) & (data['Card_Type']!=3) &(data['Card_Type']!=4)]
unused
4 CC12
11 CC04
19 CC12
23 CC12
25 CC01
...
10436 CC02
10445 CC01
10462 CC04
10465 CC01
10487 CC02
Name: Card_Type, Length: 812, dtype: object
data.replace(unused.values, 5, inplace=True)
data
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | T08 | 50000.0 | 0 | 2 | 1 | M0001 | CTY06-133 | EDC0885 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | T01 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC0565 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | T15 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-073 | EDC4639 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | T15 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC3918 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | T10 | 2500000.0 | 5 | 2 | 1 | M0001 | CTY06-133 | EDC2863 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | 2018-12-31 15:47:34.782 | T13 | 5000000.0 | 1 | 2 | 1 | M0001 | CTY06-023 | EDC0741 | OEDC0377 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | 2018-12-31 15:47:34.782 | T13 | 4800000.0 | 0 | 2 | 1 | M0001 | CTY06-181 | EDC0797 | OEDC0377 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | 2018-12-31 15:47:34.782 | T13 | 2500000.0 | 3 | 2 | 1 | M0001 | CTY06-023 | EDC0587 | OEDC0377 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | 2018-12-31 15:47:34.782 | T13 | 1100000.0 | 0 | 2 | 1 | M0001 | CTY06-186 | EDC0626 | OEDC0377 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | 2018-12-31 22:11:05.961 | T02 | 102500.0 | 0 | 2 | 2 | M0597 | CTY06-171 | EDC4477 | OEDC0633 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10500 rows × 15 columns
value_counts(data, 'Transaction_Type')
| Value | Count | Percentage |
|---|---|---|
| T01 | 2,872 | 27.35% |
| T15 | 2,449 | 23.32% |
| T02 | 1,324 | 12.61% |
| T08 | 1,074 | 10.23% |
| T13 | 769 | 7.32% |
| T06 | 677 | 6.45% |
| T14 | 509 | 4.85% |
| T04 | 264 | 2.51% |
| T03 | 207 | 1.97% |
| T10 | 112 | 1.07% |
| T12 | 77 | 0.73% |
| T11 | 50 | 0.48% |
| T20 | 29 | 0.28% |
| T17 | 27 | 0.26% |
| T05 | 22 | 0.21% |
| T16 | 12 | 0.11% |
| T09 | 8 | 0.08% |
| T07 | 7 | 0.07% |
| T19 | 7 | 0.07% |
| T18 | 4 | 0.04% |
data['Transaction_Type'].replace({'T01': 0, 'T15': 1, 'T02':2, 'T08':3}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 3 | 50000.0 | 0 | 2 | 1 | M0001 | CTY06-133 | EDC0885 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC0565 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-073 | EDC4639 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC3918 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | T10 | 2500000.0 | 5 | 2 | 1 | M0001 | CTY06-133 | EDC2863 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
unused = data['Transaction_Type'].loc[(data['Transaction_Type']!= 0)&(data['Transaction_Type']!=1)& (data['Transaction_Type']!=2) & (data['Transaction_Type']!=3)]
unused
4 T10
5 T03
6 T13
8 T06
9 T06
...
10493 T06
10495 T13
10496 T13
10497 T13
10498 T13
Name: Transaction_Type, Length: 2781, dtype: object
data.replace(unused.values, 4, inplace=True)
data
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 3 | 50000.0 | 0 | 2 | 1 | M0001 | CTY06-133 | EDC0885 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC0565 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-073 | EDC4639 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 2 | 1 | M0001 | CTY06-129 | EDC3918 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 4 | 2500000.0 | 5 | 2 | 1 | M0001 | CTY06-133 | EDC2863 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | 2018-12-31 15:47:34.782 | 4 | 5000000.0 | 1 | 2 | 1 | M0001 | CTY06-023 | EDC0741 | OEDC0377 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | 2018-12-31 15:47:34.782 | 4 | 4800000.0 | 0 | 2 | 1 | M0001 | CTY06-181 | EDC0797 | OEDC0377 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | 2018-12-31 15:47:34.782 | 4 | 2500000.0 | 3 | 2 | 1 | M0001 | CTY06-023 | EDC0587 | OEDC0377 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | 2018-12-31 15:47:34.782 | 4 | 1100000.0 | 0 | 2 | 1 | M0001 | CTY06-186 | EDC0626 | OEDC0377 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | 2018-12-31 22:11:05.961 | 2 | 102500.0 | 0 | 2 | 2 | M0597 | CTY06-171 | EDC4477 | OEDC0633 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10500 rows × 15 columns
value_counts(data, 'Card_Holder')
| Value | Count | Percentage |
|---|---|---|
| 2 | 9,788 | 93.22% |
| 1 | 712 | 6.78% |
data['Card_Holder'].replace({1: 0, 2: 1}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 3 | 50000.0 | 0 | 1 | 1 | M0001 | CTY06-133 | EDC0885 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | EDC0565 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-073 | EDC4639 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | EDC3918 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 4 | 2500000.0 | 5 | 1 | 1 | M0001 | CTY06-133 | EDC2863 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
value_counts(data, 'EDC_Type')
| Value | Count | Percentage |
|---|---|---|
| EDC0002 | 707 | 6.73% |
| EDC1846 | 25 | 0.24% |
| EDC0729 | 17 | 0.16% |
| EDC1155 | 17 | 0.16% |
| EDC4713 | 16 | 0.15% |
| EDC0798 | 16 | 0.15% |
| EDC2534 | 15 | 0.14% |
| EDC1658 | 14 | 0.13% |
| EDC1910 | 14 | 0.13% |
| EDC1104 | 14 | 0.13% |
| EDC2618 | 13 | 0.12% |
| EDC2741 | 12 | 0.11% |
| EDC3591 | 12 | 0.11% |
| EDC1243 | 12 | 0.11% |
| EDC2260 | 12 | 0.11% |
| EDC2681 | 12 | 0.11% |
| EDC3480 | 12 | 0.11% |
| EDC3397 | 12 | 0.11% |
| EDC3026 | 11 | 0.10% |
| EDC3610 | 11 | 0.10% |
| EDC0779 | 11 | 0.10% |
| EDC2770 | 11 | 0.10% |
| EDC1080 | 11 | 0.10% |
| EDC4667 | 11 | 0.10% |
| EDC1045 | 11 | 0.10% |
| EDC3866 | 11 | 0.10% |
| EDC3983 | 11 | 0.10% |
| EDC2606 | 11 | 0.10% |
| EDC2824 | 11 | 0.10% |
| EDC0080 | 11 | 0.10% |
| EDC3001 | 10 | 0.10% |
| EDC2477 | 10 | 0.10% |
| EDC3031 | 10 | 0.10% |
| EDC3730 | 10 | 0.10% |
| EDC4529 | 10 | 0.10% |
| EDC4528 | 10 | 0.10% |
| EDC0591 | 10 | 0.10% |
| EDC0711 | 10 | 0.10% |
| EDC3781 | 10 | 0.10% |
| EDC1403 | 10 | 0.10% |
| EDC3472 | 10 | 0.10% |
| EDC0379 | 10 | 0.10% |
| EDC0386 | 10 | 0.10% |
| EDC2313 | 10 | 0.10% |
| EDC1342 | 10 | 0.10% |
| EDC3508 | 9 | 0.09% |
| EDC1258 | 9 | 0.09% |
| EDC1732 | 9 | 0.09% |
| EDC2471 | 9 | 0.09% |
| EDC2889 | 9 | 0.09% |
| EDC2635 | 9 | 0.09% |
| EDC2831 | 9 | 0.09% |
| EDC3471 | 9 | 0.09% |
| EDC3703 | 9 | 0.09% |
| EDC2666 | 9 | 0.09% |
| EDC0341 | 9 | 0.09% |
| EDC3268 | 9 | 0.09% |
| EDC0890 | 9 | 0.09% |
| EDC2466 | 9 | 0.09% |
| EDC2755 | 9 | 0.09% |
| EDC0217 | 8 | 0.08% |
| EDC0942 | 8 | 0.08% |
| EDC1328 | 8 | 0.08% |
| EDC3773 | 8 | 0.08% |
| EDC2920 | 8 | 0.08% |
| EDC4716 | 8 | 0.08% |
| EDC0537 | 8 | 0.08% |
| EDC0288 | 8 | 0.08% |
| EDC1923 | 8 | 0.08% |
| EDC0796 | 8 | 0.08% |
| EDC1032 | 8 | 0.08% |
| EDC1115 | 8 | 0.08% |
| EDC4948 | 8 | 0.08% |
| EDC1145 | 8 | 0.08% |
| EDC1688 | 8 | 0.08% |
| EDC3719 | 8 | 0.08% |
| EDC1187 | 8 | 0.08% |
| EDC1391 | 8 | 0.08% |
| EDC1971 | 8 | 0.08% |
| EDC4629 | 8 | 0.08% |
| EDC1534 | 8 | 0.08% |
| EDC3814 | 8 | 0.08% |
| EDC0871 | 8 | 0.08% |
| EDC2058 | 8 | 0.08% |
| EDC3977 | 8 | 0.08% |
| EDC4581 | 8 | 0.08% |
| EDC3455 | 8 | 0.08% |
| EDC1908 | 8 | 0.08% |
| EDC0539 | 8 | 0.08% |
| EDC1055 | 8 | 0.08% |
| EDC2014 | 8 | 0.08% |
| EDC3416 | 8 | 0.08% |
| EDC1226 | 8 | 0.08% |
| EDC0574 | 8 | 0.08% |
| EDC2969 | 8 | 0.08% |
| EDC2549 | 8 | 0.08% |
| EDC0731 | 7 | 0.07% |
| EDC1495 | 7 | 0.07% |
| EDC2421 | 7 | 0.07% |
| EDC0616 | 7 | 0.07% |
| EDC1343 | 7 | 0.07% |
| EDC4373 | 7 | 0.07% |
| EDC2398 | 7 | 0.07% |
| EDC1191 | 7 | 0.07% |
| EDC3402 | 7 | 0.07% |
| EDC1669 | 7 | 0.07% |
| EDC1292 | 7 | 0.07% |
| EDC1202 | 7 | 0.07% |
| EDC1691 | 7 | 0.07% |
| EDC3605 | 7 | 0.07% |
| EDC3765 | 7 | 0.07% |
| EDC1751 | 7 | 0.07% |
| EDC0626 | 7 | 0.07% |
| EDC2385 | 7 | 0.07% |
| EDC3422 | 7 | 0.07% |
| EDC1601 | 7 | 0.07% |
| EDC3546 | 7 | 0.07% |
| EDC1186 | 7 | 0.07% |
| EDC0488 | 7 | 0.07% |
| EDC3976 | 7 | 0.07% |
| EDC1137 | 7 | 0.07% |
| EDC1537 | 7 | 0.07% |
| EDC2045 | 7 | 0.07% |
| EDC1077 | 7 | 0.07% |
| EDC1127 | 7 | 0.07% |
| EDC2387 | 7 | 0.07% |
| EDC3219 | 7 | 0.07% |
| EDC3613 | 7 | 0.07% |
| EDC1987 | 7 | 0.07% |
| EDC2611 | 7 | 0.07% |
| EDC0277 | 7 | 0.07% |
| EDC1825 | 7 | 0.07% |
| EDC1159 | 7 | 0.07% |
| EDC2480 | 7 | 0.07% |
| EDC2605 | 7 | 0.07% |
| EDC0307 | 7 | 0.07% |
| EDC2199 | 7 | 0.07% |
| EDC3384 | 6 | 0.06% |
| EDC2804 | 6 | 0.06% |
| EDC4836 | 6 | 0.06% |
| EDC0006 | 6 | 0.06% |
| EDC3326 | 6 | 0.06% |
| EDC1788 | 6 | 0.06% |
| EDC1303 | 6 | 0.06% |
| EDC0357 | 6 | 0.06% |
| EDC3054 | 6 | 0.06% |
| EDC2094 | 6 | 0.06% |
| EDC2863 | 6 | 0.06% |
| EDC0287 | 6 | 0.06% |
| EDC2936 | 6 | 0.06% |
| EDC4167 | 6 | 0.06% |
| EDC0814 | 6 | 0.06% |
| EDC0523 | 6 | 0.06% |
| EDC4289 | 6 | 0.06% |
| EDC0969 | 6 | 0.06% |
| EDC0352 | 6 | 0.06% |
| EDC2767 | 6 | 0.06% |
| EDC1472 | 6 | 0.06% |
| EDC4622 | 6 | 0.06% |
| EDC3415 | 6 | 0.06% |
| EDC0606 | 6 | 0.06% |
| EDC0380 | 6 | 0.06% |
| EDC0364 | 6 | 0.06% |
| EDC2422 | 6 | 0.06% |
| EDC1623 | 6 | 0.06% |
| EDC3614 | 6 | 0.06% |
| EDC0972 | 6 | 0.06% |
| EDC0445 | 6 | 0.06% |
| EDC4664 | 6 | 0.06% |
| EDC4727 | 6 | 0.06% |
| EDC4025 | 6 | 0.06% |
| EDC1212 | 6 | 0.06% |
| EDC0110 | 6 | 0.06% |
| EDC3033 | 6 | 0.06% |
| EDC3772 | 6 | 0.06% |
| EDC3782 | 6 | 0.06% |
| EDC1286 | 6 | 0.06% |
| EDC0257 | 6 | 0.06% |
| EDC3303 | 6 | 0.06% |
| EDC1702 | 6 | 0.06% |
| EDC1641 | 6 | 0.06% |
| EDC1421 | 6 | 0.06% |
| EDC2879 | 6 | 0.06% |
| EDC1389 | 6 | 0.06% |
| EDC1116 | 6 | 0.06% |
| EDC0821 | 6 | 0.06% |
| EDC1697 | 6 | 0.06% |
| EDC1094 | 6 | 0.06% |
| EDC0688 | 6 | 0.06% |
| EDC0554 | 6 | 0.06% |
| EDC1926 | 6 | 0.06% |
| EDC3616 | 6 | 0.06% |
| EDC2816 | 6 | 0.06% |
| EDC5141 | 6 | 0.06% |
| EDC0154 | 6 | 0.06% |
| EDC1092 | 6 | 0.06% |
| EDC0538 | 6 | 0.06% |
| EDC2431 | 6 | 0.06% |
| EDC1161 | 6 | 0.06% |
| EDC4804 | 6 | 0.06% |
| EDC0068 | 6 | 0.06% |
| EDC5271 | 6 | 0.06% |
| EDC0833 | 5 | 0.05% |
| EDC2912 | 5 | 0.05% |
| EDC2849 | 5 | 0.05% |
| EDC2348 | 5 | 0.05% |
| EDC0576 | 5 | 0.05% |
| EDC1993 | 5 | 0.05% |
| EDC2288 | 5 | 0.05% |
| EDC2171 | 5 | 0.05% |
| EDC0558 | 5 | 0.05% |
| EDC0749 | 5 | 0.05% |
| EDC1398 | 5 | 0.05% |
| EDC1306 | 5 | 0.05% |
| EDC4158 | 5 | 0.05% |
| EDC3544 | 5 | 0.05% |
| EDC0608 | 5 | 0.05% |
| EDC2067 | 5 | 0.05% |
| EDC4737 | 5 | 0.05% |
| EDC2470 | 5 | 0.05% |
| EDC3898 | 5 | 0.05% |
| EDC1711 | 5 | 0.05% |
| EDC2769 | 5 | 0.05% |
| EDC5208 | 5 | 0.05% |
| EDC3516 | 5 | 0.05% |
| EDC1130 | 5 | 0.05% |
| EDC2061 | 5 | 0.05% |
| EDC0058 | 5 | 0.05% |
| EDC4599 | 5 | 0.05% |
| EDC1037 | 5 | 0.05% |
| EDC3883 | 5 | 0.05% |
| EDC4647 | 5 | 0.05% |
| EDC0573 | 5 | 0.05% |
| EDC0579 | 5 | 0.05% |
| EDC2527 | 5 | 0.05% |
| EDC4323 | 5 | 0.05% |
| EDC4369 | 5 | 0.05% |
| EDC1714 | 5 | 0.05% |
| EDC1165 | 5 | 0.05% |
| EDC1417 | 5 | 0.05% |
| EDC4906 | 5 | 0.05% |
| EDC0001 | 5 | 0.05% |
| EDC1892 | 5 | 0.05% |
| EDC4695 | 5 | 0.05% |
| EDC1629 | 5 | 0.05% |
| EDC1523 | 5 | 0.05% |
| EDC2474 | 5 | 0.05% |
| EDC4265 | 5 | 0.05% |
| EDC4502 | 5 | 0.05% |
| EDC3593 | 5 | 0.05% |
| EDC0258 | 5 | 0.05% |
| EDC4018 | 5 | 0.05% |
| EDC3783 | 5 | 0.05% |
| EDC2031 | 5 | 0.05% |
| EDC2821 | 5 | 0.05% |
| EDC3042 | 5 | 0.05% |
| EDC3382 | 5 | 0.05% |
| EDC0301 | 5 | 0.05% |
| EDC1770 | 5 | 0.05% |
| EDC0519 | 5 | 0.05% |
| EDC1268 | 5 | 0.05% |
| EDC1162 | 5 | 0.05% |
| EDC3840 | 5 | 0.05% |
| EDC3522 | 5 | 0.05% |
| EDC0030 | 5 | 0.05% |
| EDC1166 | 5 | 0.05% |
| EDC4099 | 5 | 0.05% |
| EDC2664 | 5 | 0.05% |
| EDC2517 | 5 | 0.05% |
| EDC3659 | 5 | 0.05% |
| EDC1671 | 5 | 0.05% |
| EDC4222 | 5 | 0.05% |
| EDC0838 | 5 | 0.05% |
| EDC0314 | 5 | 0.05% |
| EDC3858 | 5 | 0.05% |
| EDC2409 | 5 | 0.05% |
| EDC4639 | 5 | 0.05% |
| EDC2428 | 5 | 0.05% |
| EDC4184 | 5 | 0.05% |
| EDC3267 | 5 | 0.05% |
| EDC3502 | 5 | 0.05% |
| EDC2746 | 5 | 0.05% |
| EDC3481 | 5 | 0.05% |
| EDC0165 | 5 | 0.05% |
| EDC1664 | 5 | 0.05% |
| EDC2293 | 5 | 0.05% |
| EDC2900 | 5 | 0.05% |
| EDC2594 | 5 | 0.05% |
| EDC2291 | 5 | 0.05% |
| EDC1909 | 5 | 0.05% |
| EDC1884 | 5 | 0.05% |
| EDC3075 | 5 | 0.05% |
| EDC3045 | 5 | 0.05% |
| EDC1738 | 5 | 0.05% |
| EDC4852 | 5 | 0.05% |
| EDC1991 | 5 | 0.05% |
| EDC4594 | 5 | 0.05% |
| EDC3345 | 5 | 0.05% |
| EDC3984 | 5 | 0.05% |
| EDC2835 | 5 | 0.05% |
| EDC2047 | 5 | 0.05% |
| EDC3553 | 5 | 0.05% |
| EDC3551 | 5 | 0.05% |
| EDC0276 | 5 | 0.05% |
| EDC4311 | 5 | 0.05% |
| EDC1160 | 5 | 0.05% |
| EDC2285 | 5 | 0.05% |
| EDC4145 | 5 | 0.05% |
| EDC2672 | 5 | 0.05% |
| EDC2139 | 5 | 0.05% |
| EDC4218 | 5 | 0.05% |
| EDC3097 | 5 | 0.05% |
| EDC3760 | 5 | 0.05% |
| EDC1763 | 5 | 0.05% |
| EDC4191 | 5 | 0.05% |
| EDC1317 | 5 | 0.05% |
| EDC3845 | 5 | 0.05% |
| EDC3643 | 5 | 0.05% |
| EDC1630 | 5 | 0.05% |
| EDC1205 | 5 | 0.05% |
| EDC1455 | 5 | 0.05% |
| EDC3503 | 5 | 0.05% |
| EDC0438 | 5 | 0.05% |
| EDC1606 | 5 | 0.05% |
| EDC1393 | 5 | 0.05% |
| EDC1927 | 5 | 0.05% |
| EDC3627 | 5 | 0.05% |
| EDC1862 | 5 | 0.05% |
| EDC0145 | 5 | 0.05% |
| EDC2641 | 5 | 0.05% |
| EDC1413 | 5 | 0.05% |
| EDC2380 | 4 | 0.04% |
| EDC1008 | 4 | 0.04% |
| EDC1136 | 4 | 0.04% |
| EDC0261 | 4 | 0.04% |
| EDC1423 | 4 | 0.04% |
| EDC2513 | 4 | 0.04% |
| EDC3584 | 4 | 0.04% |
| EDC1888 | 4 | 0.04% |
| EDC0300 | 4 | 0.04% |
| EDC0439 | 4 | 0.04% |
| EDC3717 | 4 | 0.04% |
| EDC2066 | 4 | 0.04% |
| EDC1578 | 4 | 0.04% |
| EDC1543 | 4 | 0.04% |
| EDC2944 | 4 | 0.04% |
| EDC4084 | 4 | 0.04% |
| EDC3855 | 4 | 0.04% |
| EDC3315 | 4 | 0.04% |
| EDC4100 | 4 | 0.04% |
| EDC0854 | 4 | 0.04% |
| EDC1238 | 4 | 0.04% |
| EDC3961 | 4 | 0.04% |
| EDC3065 | 4 | 0.04% |
| EDC3409 | 4 | 0.04% |
| EDC4148 | 4 | 0.04% |
| EDC3563 | 4 | 0.04% |
| EDC0003 | 4 | 0.04% |
| EDC4180 | 4 | 0.04% |
| EDC1227 | 4 | 0.04% |
| EDC0013 | 4 | 0.04% |
| EDC4994 | 4 | 0.04% |
| EDC0263 | 4 | 0.04% |
| EDC5179 | 4 | 0.04% |
| EDC0149 | 4 | 0.04% |
| EDC2833 | 4 | 0.04% |
| EDC3688 | 4 | 0.04% |
| EDC1046 | 4 | 0.04% |
| EDC3545 | 4 | 0.04% |
| EDC3302 | 4 | 0.04% |
| EDC2347 | 4 | 0.04% |
| EDC3572 | 4 | 0.04% |
| EDC2607 | 4 | 0.04% |
| EDC4077 | 4 | 0.04% |
| EDC0783 | 4 | 0.04% |
| EDC1826 | 4 | 0.04% |
| EDC1125 | 4 | 0.04% |
| EDC1112 | 4 | 0.04% |
| EDC1836 | 4 | 0.04% |
| EDC2150 | 4 | 0.04% |
| EDC4630 | 4 | 0.04% |
| EDC3232 | 4 | 0.04% |
| EDC2894 | 4 | 0.04% |
| EDC2281 | 4 | 0.04% |
| EDC0984 | 4 | 0.04% |
| EDC4137 | 4 | 0.04% |
| EDC4959 | 4 | 0.04% |
| EDC2188 | 4 | 0.04% |
| EDC0974 | 4 | 0.04% |
| EDC3190 | 4 | 0.04% |
| EDC1753 | 4 | 0.04% |
| EDC3554 | 4 | 0.04% |
| EDC2055 | 4 | 0.04% |
| EDC0570 | 4 | 0.04% |
| EDC1904 | 4 | 0.04% |
| EDC3712 | 4 | 0.04% |
| EDC3496 | 4 | 0.04% |
| EDC2248 | 4 | 0.04% |
| EDC2317 | 4 | 0.04% |
| EDC0423 | 4 | 0.04% |
| EDC3063 | 4 | 0.04% |
| EDC0543 | 4 | 0.04% |
| EDC1425 | 4 | 0.04% |
| EDC3664 | 4 | 0.04% |
| EDC0195 | 4 | 0.04% |
| EDC0456 | 4 | 0.04% |
| EDC4048 | 4 | 0.04% |
| EDC0274 | 4 | 0.04% |
| EDC0652 | 4 | 0.04% |
| EDC2596 | 4 | 0.04% |
| EDC0284 | 4 | 0.04% |
| EDC1085 | 4 | 0.04% |
| EDC4015 | 4 | 0.04% |
| EDC0166 | 4 | 0.04% |
| EDC0770 | 4 | 0.04% |
| EDC2001 | 4 | 0.04% |
| EDC1148 | 4 | 0.04% |
| EDC2445 | 4 | 0.04% |
| EDC1305 | 4 | 0.04% |
| EDC2429 | 4 | 0.04% |
| EDC4163 | 4 | 0.04% |
| EDC2929 | 4 | 0.04% |
| EDC0298 | 4 | 0.04% |
| EDC0220 | 4 | 0.04% |
| EDC1426 | 4 | 0.04% |
| EDC3787 | 4 | 0.04% |
| EDC4379 | 4 | 0.04% |
| EDC1011 | 4 | 0.04% |
| EDC4446 | 4 | 0.04% |
| EDC2845 | 4 | 0.04% |
| EDC3611 | 4 | 0.04% |
| EDC1567 | 4 | 0.04% |
| EDC3759 | 4 | 0.04% |
| EDC2828 | 4 | 0.04% |
| EDC0474 | 4 | 0.04% |
| EDC2714 | 4 | 0.04% |
| EDC2756 | 4 | 0.04% |
| EDC2289 | 4 | 0.04% |
| EDC5001 | 4 | 0.04% |
| EDC4195 | 4 | 0.04% |
| EDC1244 | 4 | 0.04% |
| EDC2012 | 4 | 0.04% |
| EDC0216 | 4 | 0.04% |
| EDC1653 | 4 | 0.04% |
| EDC1462 | 4 | 0.04% |
| EDC3136 | 4 | 0.04% |
| EDC0017 | 4 | 0.04% |
| EDC2059 | 4 | 0.04% |
| EDC2084 | 4 | 0.04% |
| EDC1667 | 4 | 0.04% |
| EDC0549 | 4 | 0.04% |
| EDC2018 | 4 | 0.04% |
| EDC1158 | 4 | 0.04% |
| EDC5097 | 4 | 0.04% |
| EDC3795 | 4 | 0.04% |
| EDC1401 | 4 | 0.04% |
| EDC2457 | 4 | 0.04% |
| EDC0098 | 4 | 0.04% |
| EDC1147 | 4 | 0.04% |
| EDC2228 | 4 | 0.04% |
| EDC3221 | 4 | 0.04% |
| EDC5145 | 4 | 0.04% |
| EDC0910 | 4 | 0.04% |
| EDC3311 | 4 | 0.04% |
| EDC3004 | 4 | 0.04% |
| EDC2551 | 4 | 0.04% |
| EDC3861 | 4 | 0.04% |
| EDC4074 | 4 | 0.04% |
| EDC3609 | 4 | 0.04% |
| EDC1140 | 4 | 0.04% |
| EDC0472 | 4 | 0.04% |
| EDC2906 | 4 | 0.04% |
| EDC4072 | 4 | 0.04% |
| EDC2406 | 4 | 0.04% |
| EDC5325 | 4 | 0.04% |
| EDC0200 | 4 | 0.04% |
| EDC2418 | 4 | 0.04% |
| EDC3306 | 4 | 0.04% |
| EDC2652 | 4 | 0.04% |
| EDC0515 | 4 | 0.04% |
| EDC1439 | 4 | 0.04% |
| EDC0510 | 4 | 0.04% |
| EDC0862 | 4 | 0.04% |
| EDC4601 | 4 | 0.04% |
| EDC3541 | 4 | 0.04% |
| EDC4878 | 4 | 0.04% |
| EDC0520 | 4 | 0.04% |
| EDC0139 | 4 | 0.04% |
| EDC3105 | 4 | 0.04% |
| EDC4916 | 4 | 0.04% |
| EDC3253 | 4 | 0.04% |
| EDC2024 | 4 | 0.04% |
| EDC4913 | 4 | 0.04% |
| EDC1869 | 4 | 0.04% |
| EDC0658 | 4 | 0.04% |
| EDC5030 | 4 | 0.04% |
| EDC2488 | 4 | 0.04% |
| EDC4122 | 4 | 0.04% |
| EDC3056 | 4 | 0.04% |
| EDC1459 | 4 | 0.04% |
| EDC3077 | 4 | 0.04% |
| EDC2265 | 4 | 0.04% |
| EDC1489 | 4 | 0.04% |
| EDC0619 | 4 | 0.04% |
| EDC1514 | 4 | 0.04% |
| EDC5019 | 4 | 0.04% |
| EDC4276 | 4 | 0.04% |
| EDC1288 | 4 | 0.04% |
| EDC2135 | 4 | 0.04% |
| EDC2589 | 4 | 0.04% |
| EDC1300 | 4 | 0.04% |
| EDC2947 | 4 | 0.04% |
| EDC1358 | 4 | 0.04% |
| EDC5013 | 4 | 0.04% |
| EDC1197 | 4 | 0.04% |
| EDC4530 | 4 | 0.04% |
| EDC1642 | 4 | 0.04% |
| EDC2256 | 4 | 0.04% |
| EDC3111 | 4 | 0.04% |
| EDC0253 | 4 | 0.04% |
| EDC1314 | 4 | 0.04% |
| EDC1052 | 4 | 0.04% |
| EDC5152 | 4 | 0.04% |
| EDC0115 | 4 | 0.04% |
| EDC3447 | 4 | 0.04% |
| EDC2982 | 4 | 0.04% |
| EDC5185 | 4 | 0.04% |
| EDC3469 | 4 | 0.04% |
| EDC1142 | 4 | 0.04% |
| EDC1099 | 4 | 0.04% |
| EDC5037 | 4 | 0.04% |
| EDC3738 | 4 | 0.04% |
| EDC2364 | 4 | 0.04% |
| EDC0455 | 4 | 0.04% |
| EDC0849 | 4 | 0.04% |
| EDC4885 | 4 | 0.04% |
| EDC2434 | 4 | 0.04% |
| EDC1860 | 4 | 0.04% |
| EDC3630 | 4 | 0.04% |
| EDC1171 | 4 | 0.04% |
| EDC1193 | 4 | 0.04% |
| EDC1454 | 4 | 0.04% |
| EDC4209 | 4 | 0.04% |
| EDC3398 | 4 | 0.04% |
| EDC1057 | 4 | 0.04% |
| EDC3918 | 4 | 0.04% |
| EDC4371 | 4 | 0.04% |
| EDC3723 | 4 | 0.04% |
| EDC3985 | 4 | 0.04% |
| EDC5205 | 4 | 0.04% |
| EDC0662 | 4 | 0.04% |
| EDC2626 | 4 | 0.04% |
| EDC1444 | 4 | 0.04% |
| EDC4069 | 4 | 0.04% |
| EDC2752 | 4 | 0.04% |
| EDC2395 | 4 | 0.04% |
| EDC4903 | 4 | 0.04% |
| EDC2027 | 4 | 0.04% |
| EDC4561 | 4 | 0.04% |
| EDC1114 | 4 | 0.04% |
| EDC3878 | 4 | 0.04% |
| EDC4701 | 4 | 0.04% |
| EDC1424 | 4 | 0.04% |
| EDC3439 | 4 | 0.04% |
| EDC2530 | 4 | 0.04% |
| EDC0930 | 4 | 0.04% |
| EDC0040 | 4 | 0.04% |
| EDC0041 | 4 | 0.04% |
| EDC4628 | 4 | 0.04% |
| EDC5291 | 4 | 0.04% |
| EDC3137 | 4 | 0.04% |
| EDC4228 | 4 | 0.04% |
| EDC2123 | 4 | 0.04% |
| EDC1645 | 4 | 0.04% |
| EDC3347 | 4 | 0.04% |
| EDC3663 | 4 | 0.04% |
| EDC0667 | 3 | 0.03% |
| EDC3744 | 3 | 0.03% |
| EDC3490 | 3 | 0.03% |
| EDC2015 | 3 | 0.03% |
| EDC4646 | 3 | 0.03% |
| EDC1407 | 3 | 0.03% |
| EDC1336 | 3 | 0.03% |
| EDC5327 | 3 | 0.03% |
| EDC3996 | 3 | 0.03% |
| EDC4947 | 3 | 0.03% |
| EDC0925 | 3 | 0.03% |
| EDC0670 | 3 | 0.03% |
| EDC4605 | 3 | 0.03% |
| EDC1662 | 3 | 0.03% |
| EDC3043 | 3 | 0.03% |
| EDC0718 | 3 | 0.03% |
| EDC2897 | 3 | 0.03% |
| EDC0516 | 3 | 0.03% |
| EDC3901 | 3 | 0.03% |
| EDC0048 | 3 | 0.03% |
| EDC3569 | 3 | 0.03% |
| EDC4512 | 3 | 0.03% |
| EDC3647 | 3 | 0.03% |
| EDC1583 | 3 | 0.03% |
| EDC1144 | 3 | 0.03% |
| EDC4260 | 3 | 0.03% |
| EDC2076 | 3 | 0.03% |
| EDC4609 | 3 | 0.03% |
| EDC1808 | 3 | 0.03% |
| EDC1469 | 3 | 0.03% |
| EDC1429 | 3 | 0.03% |
| EDC3741 | 3 | 0.03% |
| EDC1087 | 3 | 0.03% |
| EDC3163 | 3 | 0.03% |
| EDC1593 | 3 | 0.03% |
| EDC2037 | 3 | 0.03% |
| EDC2803 | 3 | 0.03% |
| EDC2292 | 3 | 0.03% |
| EDC3604 | 3 | 0.03% |
| EDC1369 | 3 | 0.03% |
| EDC1590 | 3 | 0.03% |
| EDC3990 | 3 | 0.03% |
| EDC0383 | 3 | 0.03% |
| EDC3271 | 3 | 0.03% |
| EDC0707 | 3 | 0.03% |
| EDC1372 | 3 | 0.03% |
| EDC4571 | 3 | 0.03% |
| EDC3621 | 3 | 0.03% |
| EDC3204 | 3 | 0.03% |
| EDC2838 | 3 | 0.03% |
| EDC2104 | 3 | 0.03% |
| EDC0155 | 3 | 0.03% |
| EDC0759 | 3 | 0.03% |
| EDC1813 | 3 | 0.03% |
| EDC5034 | 3 | 0.03% |
| EDC3121 | 3 | 0.03% |
| EDC4387 | 3 | 0.03% |
| EDC5293 | 3 | 0.03% |
| EDC1900 | 3 | 0.03% |
| EDC2585 | 3 | 0.03% |
| EDC5302 | 3 | 0.03% |
| EDC2286 | 3 | 0.03% |
| EDC2456 | 3 | 0.03% |
| EDC3032 | 3 | 0.03% |
| EDC3797 | 3 | 0.03% |
| EDC4572 | 3 | 0.03% |
| EDC3444 | 3 | 0.03% |
| EDC4318 | 3 | 0.03% |
| EDC4961 | 3 | 0.03% |
| EDC2473 | 3 | 0.03% |
| EDC0702 | 3 | 0.03% |
| EDC3657 | 3 | 0.03% |
| EDC2371 | 3 | 0.03% |
| EDC2593 | 3 | 0.03% |
| EDC2108 | 3 | 0.03% |
| EDC2554 | 3 | 0.03% |
| EDC1696 | 3 | 0.03% |
| EDC4635 | 3 | 0.03% |
| EDC1379 | 3 | 0.03% |
| EDC1146 | 3 | 0.03% |
| EDC0427 | 3 | 0.03% |
| EDC1388 | 3 | 0.03% |
| EDC2439 | 3 | 0.03% |
| EDC4954 | 3 | 0.03% |
| EDC3468 | 3 | 0.03% |
| EDC4486 | 3 | 0.03% |
| EDC1477 | 3 | 0.03% |
| EDC0807 | 3 | 0.03% |
| EDC3098 | 3 | 0.03% |
| EDC0194 | 3 | 0.03% |
| EDC1017 | 3 | 0.03% |
| EDC0085 | 3 | 0.03% |
| EDC0764 | 3 | 0.03% |
| EDC5338 | 3 | 0.03% |
| EDC0065 | 3 | 0.03% |
| EDC4612 | 3 | 0.03% |
| EDC1787 | 3 | 0.03% |
| EDC3745 | 3 | 0.03% |
| EDC0168 | 3 | 0.03% |
| EDC3685 | 3 | 0.03% |
| EDC4936 | 3 | 0.03% |
| EDC2411 | 3 | 0.03% |
| EDC1365 | 3 | 0.03% |
| EDC1170 | 3 | 0.03% |
| EDC0265 | 3 | 0.03% |
| EDC0699 | 3 | 0.03% |
| EDC0743 | 3 | 0.03% |
| EDC0727 | 3 | 0.03% |
| EDC2535 | 3 | 0.03% |
| EDC2913 | 3 | 0.03% |
| EDC1496 | 3 | 0.03% |
| EDC2798 | 3 | 0.03% |
| EDC3379 | 3 | 0.03% |
| EDC3777 | 3 | 0.03% |
| EDC0812 | 3 | 0.03% |
| EDC4421 | 3 | 0.03% |
| EDC1573 | 3 | 0.03% |
| EDC2103 | 3 | 0.03% |
| EDC2506 | 3 | 0.03% |
| EDC4871 | 3 | 0.03% |
| EDC3278 | 3 | 0.03% |
| EDC4352 | 3 | 0.03% |
| EDC4244 | 3 | 0.03% |
| EDC4165 | 3 | 0.03% |
| EDC4591 | 3 | 0.03% |
| EDC3388 | 3 | 0.03% |
| EDC4600 | 3 | 0.03% |
| EDC3517 | 3 | 0.03% |
| EDC2177 | 3 | 0.03% |
| EDC0141 | 3 | 0.03% |
| EDC2249 | 3 | 0.03% |
| EDC0600 | 3 | 0.03% |
| EDC4263 | 3 | 0.03% |
| EDC5262 | 3 | 0.03% |
| EDC3785 | 3 | 0.03% |
| EDC3400 | 3 | 0.03% |
| EDC0609 | 3 | 0.03% |
| EDC2128 | 3 | 0.03% |
| EDC0193 | 3 | 0.03% |
| EDC1312 | 3 | 0.03% |
| EDC0011 | 3 | 0.03% |
| EDC3010 | 3 | 0.03% |
| EDC1532 | 3 | 0.03% |
| EDC1811 | 3 | 0.03% |
| EDC5113 | 3 | 0.03% |
| EDC0856 | 3 | 0.03% |
| EDC1340 | 3 | 0.03% |
| EDC1105 | 3 | 0.03% |
| EDC0221 | 3 | 0.03% |
| EDC0161 | 3 | 0.03% |
| EDC0339 | 3 | 0.03% |
| EDC2074 | 3 | 0.03% |
| EDC3843 | 3 | 0.03% |
| EDC0701 | 3 | 0.03% |
| EDC0753 | 3 | 0.03% |
| EDC3989 | 3 | 0.03% |
| EDC4317 | 3 | 0.03% |
| EDC4826 | 3 | 0.03% |
| EDC2573 | 3 | 0.03% |
| EDC1386 | 3 | 0.03% |
| EDC4866 | 3 | 0.03% |
| EDC2287 | 3 | 0.03% |
| EDC4975 | 3 | 0.03% |
| EDC4771 | 3 | 0.03% |
| EDC2290 | 3 | 0.03% |
| EDC0425 | 3 | 0.03% |
| EDC5300 | 3 | 0.03% |
| EDC1538 | 3 | 0.03% |
| EDC1734 | 3 | 0.03% |
| EDC0521 | 3 | 0.03% |
| EDC2225 | 3 | 0.03% |
| EDC2608 | 3 | 0.03% |
| EDC3578 | 3 | 0.03% |
| EDC4285 | 3 | 0.03% |
| EDC4689 | 3 | 0.03% |
| EDC5322 | 3 | 0.03% |
| EDC3881 | 3 | 0.03% |
| EDC2617 | 3 | 0.03% |
| EDC3495 | 3 | 0.03% |
| EDC0138 | 3 | 0.03% |
| EDC2025 | 3 | 0.03% |
| EDC1301 | 3 | 0.03% |
| EDC3895 | 3 | 0.03% |
| EDC2724 | 3 | 0.03% |
| EDC1822 | 3 | 0.03% |
| EDC4091 | 3 | 0.03% |
| EDC1980 | 3 | 0.03% |
| EDC0433 | 3 | 0.03% |
| EDC2781 | 3 | 0.03% |
| EDC0583 | 3 | 0.03% |
| EDC4130 | 3 | 0.03% |
| EDC0971 | 3 | 0.03% |
| EDC4206 | 3 | 0.03% |
| EDC4192 | 3 | 0.03% |
| EDC3269 | 3 | 0.03% |
| EDC4501 | 3 | 0.03% |
| EDC3573 | 3 | 0.03% |
| EDC1259 | 3 | 0.03% |
| EDC4155 | 3 | 0.03% |
| EDC1023 | 3 | 0.03% |
| EDC0927 | 3 | 0.03% |
| EDC0181 | 3 | 0.03% |
| EDC1529 | 3 | 0.03% |
| EDC1786 | 3 | 0.03% |
| EDC3966 | 3 | 0.03% |
| EDC1833 | 3 | 0.03% |
| EDC4007 | 3 | 0.03% |
| EDC4309 | 3 | 0.03% |
| EDC5199 | 3 | 0.03% |
| EDC0873 | 3 | 0.03% |
| EDC4221 | 3 | 0.03% |
| EDC4437 | 3 | 0.03% |
| EDC1740 | 3 | 0.03% |
| EDC1674 | 3 | 0.03% |
| EDC0967 | 3 | 0.03% |
| EDC4526 | 3 | 0.03% |
| EDC1728 | 3 | 0.03% |
| EDC4636 | 3 | 0.03% |
| EDC1183 | 3 | 0.03% |
| EDC1615 | 3 | 0.03% |
| EDC4227 | 3 | 0.03% |
| EDC0563 | 3 | 0.03% |
| EDC4765 | 3 | 0.03% |
| EDC1690 | 3 | 0.03% |
| EDC4652 | 3 | 0.03% |
| EDC4368 | 3 | 0.03% |
| EDC0305 | 3 | 0.03% |
| EDC0073 | 3 | 0.03% |
| EDC0081 | 3 | 0.03% |
| EDC4868 | 3 | 0.03% |
| EDC4185 | 3 | 0.03% |
| EDC0297 | 3 | 0.03% |
| EDC1585 | 3 | 0.03% |
| EDC3484 | 3 | 0.03% |
| EDC1896 | 3 | 0.03% |
| EDC2306 | 3 | 0.03% |
| EDC1215 | 3 | 0.03% |
| EDC4621 | 3 | 0.03% |
| EDC0326 | 3 | 0.03% |
| EDC3316 | 3 | 0.03% |
| EDC3485 | 3 | 0.03% |
| EDC0622 | 3 | 0.03% |
| EDC1548 | 3 | 0.03% |
| EDC3420 | 3 | 0.03% |
| EDC1182 | 3 | 0.03% |
| EDC0697 | 3 | 0.03% |
| EDC4333 | 3 | 0.03% |
| EDC1746 | 3 | 0.03% |
| EDC1564 | 3 | 0.03% |
| EDC3500 | 3 | 0.03% |
| EDC1970 | 3 | 0.03% |
| EDC2668 | 3 | 0.03% |
| EDC0184 | 3 | 0.03% |
| EDC0655 | 3 | 0.03% |
| EDC4730 | 3 | 0.03% |
| EDC1103 | 3 | 0.03% |
| EDC1333 | 3 | 0.03% |
| EDC1755 | 3 | 0.03% |
| EDC1761 | 3 | 0.03% |
| EDC3356 | 3 | 0.03% |
| EDC4292 | 3 | 0.03% |
| EDC2521 | 3 | 0.03% |
| EDC0078 | 3 | 0.03% |
| EDC2144 | 3 | 0.03% |
| EDC2576 | 3 | 0.03% |
| EDC0792 | 3 | 0.03% |
| EDC1676 | 3 | 0.03% |
| EDC4315 | 3 | 0.03% |
| EDC1241 | 3 | 0.03% |
| EDC1416 | 3 | 0.03% |
| EDC4567 | 3 | 0.03% |
| EDC4835 | 3 | 0.03% |
| EDC4527 | 3 | 0.03% |
| EDC1373 | 3 | 0.03% |
| EDC1586 | 3 | 0.03% |
| EDC3459 | 3 | 0.03% |
| EDC1381 | 3 | 0.03% |
| EDC2381 | 3 | 0.03% |
| EDC0817 | 3 | 0.03% |
| EDC1151 | 3 | 0.03% |
| EDC1644 | 3 | 0.03% |
| EDC2378 | 3 | 0.03% |
| EDC0406 | 3 | 0.03% |
| EDC1364 | 3 | 0.03% |
| EDC1680 | 3 | 0.03% |
| EDC1461 | 3 | 0.03% |
| EDC2979 | 3 | 0.03% |
| EDC4769 | 3 | 0.03% |
| EDC0463 | 3 | 0.03% |
| EDC5164 | 3 | 0.03% |
| EDC1798 | 3 | 0.03% |
| EDC0478 | 3 | 0.03% |
| EDC2825 | 3 | 0.03% |
| EDC0883 | 3 | 0.03% |
| EDC1203 | 3 | 0.03% |
| EDC2615 | 3 | 0.03% |
| EDC0717 | 3 | 0.03% |
| EDC5101 | 3 | 0.03% |
| EDC0204 | 3 | 0.03% |
| EDC2311 | 3 | 0.03% |
| EDC3804 | 3 | 0.03% |
| EDC2539 | 3 | 0.03% |
| EDC3967 | 3 | 0.03% |
| EDC1405 | 3 | 0.03% |
| EDC3702 | 3 | 0.03% |
| EDC1350 | 3 | 0.03% |
| EDC2853 | 3 | 0.03% |
| EDC0720 | 3 | 0.03% |
| EDC1074 | 3 | 0.03% |
| EDC1295 | 3 | 0.03% |
| EDC3656 | 3 | 0.03% |
| EDC1221 | 3 | 0.03% |
| EDC0797 | 3 | 0.03% |
| EDC1492 | 3 | 0.03% |
| EDC3803 | 3 | 0.03% |
| EDC0353 | 3 | 0.03% |
| EDC0976 | 3 | 0.03% |
| EDC4718 | 3 | 0.03% |
| EDC1915 | 3 | 0.03% |
| EDC0572 | 3 | 0.03% |
| EDC3559 | 3 | 0.03% |
| EDC1329 | 3 | 0.03% |
| EDC1154 | 3 | 0.03% |
| EDC3171 | 3 | 0.03% |
| EDC4017 | 3 | 0.03% |
| EDC2042 | 3 | 0.03% |
| EDC3131 | 3 | 0.03% |
| EDC0322 | 3 | 0.03% |
| EDC0762 | 3 | 0.03% |
| EDC1065 | 3 | 0.03% |
| EDC3638 | 3 | 0.03% |
| EDC2172 | 3 | 0.03% |
| EDC2562 | 3 | 0.03% |
| EDC2601 | 3 | 0.03% |
| EDC3074 | 3 | 0.03% |
| EDC3968 | 3 | 0.03% |
| EDC2230 | 3 | 0.03% |
| EDC0534 | 3 | 0.03% |
| EDC3624 | 3 | 0.03% |
| EDC2866 | 3 | 0.03% |
| EDC2064 | 3 | 0.03% |
| EDC5202 | 3 | 0.03% |
| EDC3637 | 3 | 0.03% |
| EDC3810 | 3 | 0.03% |
| EDC3110 | 3 | 0.03% |
| EDC2550 | 3 | 0.03% |
| EDC0088 | 3 | 0.03% |
| EDC2758 | 3 | 0.03% |
| EDC2389 | 3 | 0.03% |
| EDC2962 | 3 | 0.03% |
| EDC2325 | 3 | 0.03% |
| EDC0998 | 3 | 0.03% |
| EDC0607 | 3 | 0.03% |
| EDC3749 | 3 | 0.03% |
| EDC2710 | 3 | 0.03% |
| EDC1120 | 3 | 0.03% |
| EDC3802 | 3 | 0.03% |
| EDC3555 | 3 | 0.03% |
| EDC3640 | 3 | 0.03% |
| EDC5089 | 3 | 0.03% |
| EDC2914 | 3 | 0.03% |
| EDC4452 | 3 | 0.03% |
| EDC4669 | 3 | 0.03% |
| EDC1902 | 3 | 0.03% |
| EDC1774 | 3 | 0.03% |
| EDC3673 | 3 | 0.03% |
| EDC1820 | 3 | 0.03% |
| EDC3711 | 3 | 0.03% |
| EDC1457 | 3 | 0.03% |
| EDC2674 | 3 | 0.03% |
| EDC0560 | 3 | 0.03% |
| EDC5311 | 3 | 0.03% |
| EDC3304 | 3 | 0.03% |
| EDC0077 | 3 | 0.03% |
| EDC5104 | 3 | 0.03% |
| EDC4851 | 3 | 0.03% |
| EDC2088 | 3 | 0.03% |
| EDC2478 | 3 | 0.03% |
| EDC0090 | 3 | 0.03% |
| EDC5050 | 3 | 0.03% |
| EDC4382 | 3 | 0.03% |
| EDC2599 | 3 | 0.03% |
| EDC1672 | 3 | 0.03% |
| EDC0421 | 3 | 0.03% |
| EDC1602 | 3 | 0.03% |
| EDC4171 | 3 | 0.03% |
| EDC3602 | 3 | 0.03% |
| EDC5259 | 3 | 0.03% |
| EDC3079 | 3 | 0.03% |
| EDC3953 | 3 | 0.03% |
| EDC0852 | 3 | 0.03% |
| EDC2368 | 3 | 0.03% |
| EDC0705 | 3 | 0.03% |
| EDC5231 | 3 | 0.03% |
| EDC4711 | 3 | 0.03% |
| EDC4422 | 3 | 0.03% |
| EDC3960 | 3 | 0.03% |
| EDC0735 | 3 | 0.03% |
| EDC0236 | 3 | 0.03% |
| EDC3848 | 3 | 0.03% |
| EDC1079 | 3 | 0.03% |
| EDC2447 | 3 | 0.03% |
| EDC1153 | 3 | 0.03% |
| EDC4604 | 3 | 0.03% |
| EDC2402 | 3 | 0.03% |
| EDC1608 | 3 | 0.03% |
| EDC2600 | 3 | 0.03% |
| EDC1976 | 3 | 0.03% |
| EDC4199 | 3 | 0.03% |
| EDC1509 | 3 | 0.03% |
| EDC2257 | 3 | 0.03% |
| EDC2120 | 3 | 0.03% |
| EDC4837 | 3 | 0.03% |
| EDC1349 | 3 | 0.03% |
| EDC0813 | 3 | 0.03% |
| EDC5298 | 3 | 0.03% |
| EDC1654 | 3 | 0.03% |
| EDC1895 | 3 | 0.03% |
| EDC0636 | 3 | 0.03% |
| EDC4372 | 3 | 0.03% |
| EDC0851 | 3 | 0.03% |
| EDC3722 | 3 | 0.03% |
| EDC2282 | 3 | 0.03% |
| EDC2237 | 3 | 0.03% |
| EDC4006 | 3 | 0.03% |
| EDC0659 | 3 | 0.03% |
| EDC3982 | 3 | 0.03% |
| EDC0977 | 3 | 0.03% |
| EDC0049 | 3 | 0.03% |
| EDC1866 | 3 | 0.03% |
| EDC1775 | 3 | 0.03% |
| EDC4458 | 3 | 0.03% |
| EDC3476 | 3 | 0.03% |
| EDC3027 | 3 | 0.03% |
| EDC0544 | 3 | 0.03% |
| EDC4660 | 3 | 0.03% |
| EDC4264 | 3 | 0.03% |
| EDC3041 | 3 | 0.03% |
| EDC3904 | 3 | 0.03% |
| EDC0768 | 3 | 0.03% |
| EDC1370 | 3 | 0.03% |
| EDC1871 | 3 | 0.03% |
| EDC2098 | 3 | 0.03% |
| EDC4792 | 3 | 0.03% |
| EDC2904 | 3 | 0.03% |
| EDC1650 | 3 | 0.03% |
| EDC1571 | 3 | 0.03% |
| EDC1880 | 3 | 0.03% |
| EDC4016 | 3 | 0.03% |
| EDC1840 | 3 | 0.03% |
| EDC3880 | 3 | 0.03% |
| EDC1515 | 3 | 0.03% |
| EDC5232 | 3 | 0.03% |
| EDC0306 | 3 | 0.03% |
| EDC5146 | 3 | 0.03% |
| EDC2961 | 3 | 0.03% |
| EDC2848 | 3 | 0.03% |
| EDC3108 | 3 | 0.03% |
| EDC2933 | 3 | 0.03% |
| EDC3720 | 3 | 0.03% |
| EDC5061 | 3 | 0.03% |
| EDC4535 | 3 | 0.03% |
| EDC2080 | 3 | 0.03% |
| EDC4284 | 3 | 0.03% |
| EDC0709 | 3 | 0.03% |
| EDC3155 | 3 | 0.03% |
| EDC5129 | 3 | 0.03% |
| EDC0732 | 3 | 0.03% |
| EDC0694 | 3 | 0.03% |
| EDC2505 | 3 | 0.03% |
| EDC0693 | 3 | 0.03% |
| EDC1267 | 3 | 0.03% |
| EDC3599 | 3 | 0.03% |
| EDC1219 | 3 | 0.03% |
| EDC4684 | 3 | 0.03% |
| EDC4290 | 3 | 0.03% |
| EDC3376 | 3 | 0.03% |
| EDC4202 | 3 | 0.03% |
| EDC4162 | 3 | 0.03% |
| EDC3103 | 3 | 0.03% |
| EDC4067 | 3 | 0.03% |
| EDC1001 | 3 | 0.03% |
| EDC5239 | 3 | 0.03% |
| EDC4034 | 3 | 0.03% |
| EDC4089 | 3 | 0.03% |
| EDC0147 | 3 | 0.03% |
| EDC1309 | 3 | 0.03% |
| EDC2057 | 3 | 0.03% |
| EDC0754 | 3 | 0.03% |
| EDC2732 | 3 | 0.03% |
| EDC2536 | 3 | 0.03% |
| EDC1479 | 3 | 0.03% |
| EDC0956 | 3 | 0.03% |
| EDC4508 | 3 | 0.03% |
| EDC1501 | 3 | 0.03% |
| EDC0286 | 3 | 0.03% |
| EDC0669 | 3 | 0.03% |
| EDC0302 | 3 | 0.03% |
| EDC3852 | 3 | 0.03% |
| EDC4121 | 3 | 0.03% |
| EDC3780 | 3 | 0.03% |
| EDC1588 | 3 | 0.03% |
| EDC2807 | 3 | 0.03% |
| EDC2303 | 3 | 0.03% |
| EDC4377 | 3 | 0.03% |
| EDC0941 | 3 | 0.03% |
| EDC1766 | 3 | 0.03% |
| EDC0496 | 3 | 0.03% |
| EDC1093 | 3 | 0.03% |
| EDC1440 | 3 | 0.03% |
| EDC1447 | 3 | 0.03% |
| EDC1026 | 3 | 0.03% |
| EDC3072 | 3 | 0.03% |
| EDC2532 | 3 | 0.03% |
| EDC3991 | 3 | 0.03% |
| EDC5094 | 3 | 0.03% |
| EDC2365 | 3 | 0.03% |
| EDC3632 | 3 | 0.03% |
| EDC2215 | 3 | 0.03% |
| EDC2491 | 3 | 0.03% |
| EDC1565 | 3 | 0.03% |
| EDC0550 | 3 | 0.03% |
| EDC5341 | 3 | 0.03% |
| EDC4375 | 3 | 0.03% |
| EDC2869 | 3 | 0.03% |
| EDC5122 | 3 | 0.03% |
| EDC4946 | 3 | 0.03% |
| EDC1253 | 3 | 0.03% |
| EDC1528 | 3 | 0.03% |
| EDC2490 | 3 | 0.03% |
| EDC0255 | 3 | 0.03% |
| EDC1505 | 3 | 0.03% |
| EDC0965 | 3 | 0.03% |
| EDC0863 | 3 | 0.03% |
| EDC0935 | 3 | 0.03% |
| EDC0584 | 3 | 0.03% |
| EDC1512 | 3 | 0.03% |
| EDC2352 | 3 | 0.03% |
| EDC0343 | 3 | 0.03% |
| EDC1084 | 3 | 0.03% |
| EDC5074 | 3 | 0.03% |
| EDC0378 | 3 | 0.03% |
| EDC2682 | 3 | 0.03% |
| EDC3437 | 2 | 0.02% |
| EDC3518 | 2 | 0.02% |
| EDC2727 | 2 | 0.02% |
| EDC0999 | 2 | 0.02% |
| EDC3756 | 2 | 0.02% |
| EDC0532 | 2 | 0.02% |
| EDC1890 | 2 | 0.02% |
| EDC1022 | 2 | 0.02% |
| EDC0565 | 2 | 0.02% |
| EDC1510 | 2 | 0.02% |
| EDC3309 | 2 | 0.02% |
| EDC0597 | 2 | 0.02% |
| EDC4856 | 2 | 0.02% |
| EDC3150 | 2 | 0.02% |
| EDC3993 | 2 | 0.02% |
| EDC0891 | 2 | 0.02% |
| EDC5214 | 2 | 0.02% |
| EDC3383 | 2 | 0.02% |
| EDC4212 | 2 | 0.02% |
| EDC2538 | 2 | 0.02% |
| EDC3348 | 2 | 0.02% |
| EDC3310 | 2 | 0.02% |
| EDC0279 | 2 | 0.02% |
| EDC0992 | 2 | 0.02% |
| EDC4366 | 2 | 0.02% |
| EDC1685 | 2 | 0.02% |
| EDC4051 | 2 | 0.02% |
| EDC3073 | 2 | 0.02% |
| EDC5316 | 2 | 0.02% |
| EDC0940 | 2 | 0.02% |
| EDC3014 | 2 | 0.02% |
| EDC1128 | 2 | 0.02% |
| EDC2749 | 2 | 0.02% |
| EDC2658 | 2 | 0.02% |
| EDC0968 | 2 | 0.02% |
| EDC0395 | 2 | 0.02% |
| EDC4055 | 2 | 0.02% |
| EDC2068 | 2 | 0.02% |
| EDC1195 | 2 | 0.02% |
| EDC5099 | 2 | 0.02% |
| EDC4419 | 2 | 0.02% |
| EDC4247 | 2 | 0.02% |
| EDC3568 | 2 | 0.02% |
| EDC4039 | 2 | 0.02% |
| EDC3375 | 2 | 0.02% |
| EDC4516 | 2 | 0.02% |
| EDC0117 | 2 | 0.02% |
| EDC3208 | 2 | 0.02% |
| EDC0063 | 2 | 0.02% |
| EDC3585 | 2 | 0.02% |
| EDC4028 | 2 | 0.02% |
| EDC3549 | 2 | 0.02% |
| EDC2367 | 2 | 0.02% |
| EDC3998 | 2 | 0.02% |
| EDC4717 | 2 | 0.02% |
| EDC0132 | 2 | 0.02% |
| EDC0566 | 2 | 0.02% |
| EDC0361 | 2 | 0.02% |
| EDC1133 | 2 | 0.02% |
| EDC0605 | 2 | 0.02% |
| EDC2400 | 2 | 0.02% |
| EDC5307 | 2 | 0.02% |
| EDC3592 | 2 | 0.02% |
| EDC2095 | 2 | 0.02% |
| EDC1945 | 2 | 0.02% |
| EDC4757 | 2 | 0.02% |
| EDC2772 | 2 | 0.02% |
| EDC1332 | 2 | 0.02% |
| EDC1648 | 2 | 0.02% |
| EDC3457 | 2 | 0.02% |
| EDC3451 | 2 | 0.02% |
| EDC2343 | 2 | 0.02% |
| EDC2999 | 2 | 0.02% |
| EDC5225 | 2 | 0.02% |
| EDC0111 | 2 | 0.02% |
| EDC0973 | 2 | 0.02% |
| EDC4957 | 2 | 0.02% |
| EDC2494 | 2 | 0.02% |
| EDC3975 | 2 | 0.02% |
| EDC0075 | 2 | 0.02% |
| EDC5072 | 2 | 0.02% |
| EDC2099 | 2 | 0.02% |
| EDC2109 | 2 | 0.02% |
| EDC0878 | 2 | 0.02% |
| EDC2901 | 2 | 0.02% |
| EDC1978 | 2 | 0.02% |
| EDC4389 | 2 | 0.02% |
| EDC4081 | 2 | 0.02% |
| EDC4353 | 2 | 0.02% |
| EDC1972 | 2 | 0.02% |
| EDC1095 | 2 | 0.02% |
| EDC2200 | 2 | 0.02% |
| EDC0262 | 2 | 0.02% |
| EDC4867 | 2 | 0.02% |
| EDC1420 | 2 | 0.02% |
| EDC4677 | 2 | 0.02% |
| EDC3037 | 2 | 0.02% |
| EDC3902 | 2 | 0.02% |
| EDC0822 | 2 | 0.02% |
| EDC0007 | 2 | 0.02% |
| EDC2629 | 2 | 0.02% |
| EDC4441 | 2 | 0.02% |
| EDC1460 | 2 | 0.02% |
| EDC1056 | 2 | 0.02% |
| EDC2954 | 2 | 0.02% |
| EDC4863 | 2 | 0.02% |
| EDC0403 | 2 | 0.02% |
| EDC2779 | 2 | 0.02% |
| EDC2043 | 2 | 0.02% |
| EDC1354 | 2 | 0.02% |
| EDC1778 | 2 | 0.02% |
| EDC0580 | 2 | 0.02% |
| EDC0387 | 2 | 0.02% |
| EDC2036 | 2 | 0.02% |
| EDC4141 | 2 | 0.02% |
| EDC2989 | 2 | 0.02% |
| EDC1240 | 2 | 0.02% |
| EDC4857 | 2 | 0.02% |
| EDC1854 | 2 | 0.02% |
| EDC1684 | 2 | 0.02% |
| EDC4361 | 2 | 0.02% |
| EDC1410 | 2 | 0.02% |
| EDC5031 | 2 | 0.02% |
| EDC4642 | 2 | 0.02% |
| EDC4839 | 2 | 0.02% |
| EDC2391 | 2 | 0.02% |
| EDC4330 | 2 | 0.02% |
| EDC2483 | 2 | 0.02% |
| EDC1934 | 2 | 0.02% |
| EDC2112 | 2 | 0.02% |
| EDC3888 | 2 | 0.02% |
| EDC5010 | 2 | 0.02% |
| EDC3707 | 2 | 0.02% |
| EDC1507 | 2 | 0.02% |
| EDC2627 | 2 | 0.02% |
| EDC1484 | 2 | 0.02% |
| EDC2898 | 2 | 0.02% |
| EDC3776 | 2 | 0.02% |
| EDC2016 | 2 | 0.02% |
| EDC2425 | 2 | 0.02% |
| EDC2722 | 2 | 0.02% |
| EDC1957 | 2 | 0.02% |
| EDC1383 | 2 | 0.02% |
| EDC0765 | 2 | 0.02% |
| EDC4828 | 2 | 0.02% |
| EDC2985 | 2 | 0.02% |
| EDC1557 | 2 | 0.02% |
| EDC3367 | 2 | 0.02% |
| EDC1179 | 2 | 0.02% |
| EDC2124 | 2 | 0.02% |
| EDC0244 | 2 | 0.02% |
| EDC4673 | 2 | 0.02% |
| EDC4849 | 2 | 0.02% |
| EDC1471 | 2 | 0.02% |
| EDC2990 | 2 | 0.02% |
| EDC1660 | 2 | 0.02% |
| EDC1100 | 2 | 0.02% |
| EDC4038 | 2 | 0.02% |
| EDC1156 | 2 | 0.02% |
| EDC2684 | 2 | 0.02% |
| EDC4703 | 2 | 0.02% |
| EDC4728 | 2 | 0.02% |
| EDC2747 | 2 | 0.02% |
| EDC4063 | 2 | 0.02% |
| EDC0777 | 2 | 0.02% |
| EDC2678 | 2 | 0.02% |
| EDC3144 | 2 | 0.02% |
| EDC3695 | 2 | 0.02% |
| EDC1589 | 2 | 0.02% |
| EDC4475 | 2 | 0.02% |
| EDC1784 | 2 | 0.02% |
| EDC1983 | 2 | 0.02% |
| EDC0256 | 2 | 0.02% |
| EDC4798 | 2 | 0.02% |
| EDC2935 | 2 | 0.02% |
| EDC1351 | 2 | 0.02% |
| EDC2113 | 2 | 0.02% |
| EDC5224 | 2 | 0.02% |
| EDC4175 | 2 | 0.02% |
| EDC5321 | 2 | 0.02% |
| EDC1091 | 2 | 0.02% |
| EDC0234 | 2 | 0.02% |
| EDC0536 | 2 | 0.02% |
| EDC1040 | 2 | 0.02% |
| EDC0728 | 2 | 0.02% |
| EDC1276 | 2 | 0.02% |
| EDC4980 | 2 | 0.02% |
| EDC2884 | 2 | 0.02% |
| EDC1867 | 2 | 0.02% |
| EDC4054 | 2 | 0.02% |
| EDC0569 | 2 | 0.02% |
| EDC2077 | 2 | 0.02% |
| EDC4133 | 2 | 0.02% |
| EDC4651 | 2 | 0.02% |
| EDC1302 | 2 | 0.02% |
| EDC2062 | 2 | 0.02% |
| EDC3691 | 2 | 0.02% |
| EDC1699 | 2 | 0.02% |
| EDC1992 | 2 | 0.02% |
| EDC2438 | 2 | 0.02% |
| EDC4399 | 2 | 0.02% |
| EDC0848 | 2 | 0.02% |
| EDC2270 | 2 | 0.02% |
| EDC0465 | 2 | 0.02% |
| EDC1819 | 2 | 0.02% |
| EDC4335 | 2 | 0.02% |
| EDC0808 | 2 | 0.02% |
| EDC1347 | 2 | 0.02% |
| EDC1715 | 2 | 0.02% |
| EDC1997 | 2 | 0.02% |
| EDC5249 | 2 | 0.02% |
| EDC1322 | 2 | 0.02% |
| EDC4845 | 2 | 0.02% |
| EDC2911 | 2 | 0.02% |
| EDC3619 | 2 | 0.02% |
| EDC5056 | 2 | 0.02% |
| EDC4249 | 2 | 0.02% |
| EDC4595 | 2 | 0.02% |
| EDC2217 | 2 | 0.02% |
| EDC1223 | 2 | 0.02% |
| EDC3646 | 2 | 0.02% |
| EDC4712 | 2 | 0.02% |
| EDC1408 | 2 | 0.02% |
| EDC0587 | 2 | 0.02% |
| EDC4088 | 2 | 0.02% |
| EDC3934 | 2 | 0.02% |
| EDC3497 | 2 | 0.02% |
| EDC0958 | 2 | 0.02% |
| EDC3038 | 2 | 0.02% |
| EDC1432 | 2 | 0.02% |
| EDC0578 | 2 | 0.02% |
| EDC3289 | 2 | 0.02% |
| EDC2998 | 2 | 0.02% |
| EDC3426 | 2 | 0.02% |
| EDC1003 | 2 | 0.02% |
| EDC5236 | 2 | 0.02% |
| EDC0760 | 2 | 0.02% |
| EDC2443 | 2 | 0.02% |
| EDC2502 | 2 | 0.02% |
| EDC3373 | 2 | 0.02% |
| EDC5329 | 2 | 0.02% |
| EDC1177 | 2 | 0.02% |
| EDC3580 | 2 | 0.02% |
| EDC2050 | 2 | 0.02% |
| EDC2205 | 2 | 0.02% |
| EDC3332 | 2 | 0.02% |
| EDC5289 | 2 | 0.02% |
| EDC0175 | 2 | 0.02% |
| EDC3483 | 2 | 0.02% |
| EDC4200 | 2 | 0.02% |
| EDC2198 | 2 | 0.02% |
| EDC3319 | 2 | 0.02% |
| EDC2163 | 2 | 0.02% |
| EDC2330 | 2 | 0.02% |
| EDC2976 | 2 | 0.02% |
| EDC4671 | 2 | 0.02% |
| EDC2973 | 2 | 0.02% |
| EDC5058 | 2 | 0.02% |
| EDC2479 | 2 | 0.02% |
| EDC1873 | 2 | 0.02% |
| EDC2321 | 2 | 0.02% |
| EDC4495 | 2 | 0.02% |
| EDC3626 | 2 | 0.02% |
| EDC2087 | 2 | 0.02% |
| EDC1943 | 2 | 0.02% |
| EDC4658 | 2 | 0.02% |
| EDC1069 | 2 | 0.02% |
| EDC1433 | 2 | 0.02% |
| EDC1741 | 2 | 0.02% |
| EDC3013 | 2 | 0.02% |
| EDC0903 | 2 | 0.02% |
| EDC0847 | 2 | 0.02% |
| EDC1024 | 2 | 0.02% |
| EDC0038 | 2 | 0.02% |
| EDC0635 | 2 | 0.02% |
| EDC3335 | 2 | 0.02% |
| EDC2552 | 2 | 0.02% |
| EDC1705 | 2 | 0.02% |
| EDC4815 | 2 | 0.02% |
| EDC3116 | 2 | 0.02% |
| EDC4657 | 2 | 0.02% |
| EDC5257 | 2 | 0.02% |
| EDC4457 | 2 | 0.02% |
| EDC0269 | 2 | 0.02% |
| EDC4120 | 2 | 0.02% |
| EDC1939 | 2 | 0.02% |
| EDC3280 | 2 | 0.02% |
| EDC2442 | 2 | 0.02% |
| EDC0567 | 2 | 0.02% |
| EDC0680 | 2 | 0.02% |
| EDC2691 | 2 | 0.02% |
| EDC2895 | 2 | 0.02% |
| EDC2765 | 2 | 0.02% |
| EDC5039 | 2 | 0.02% |
| EDC1028 | 2 | 0.02% |
| EDC0461 | 2 | 0.02% |
| EDC2854 | 2 | 0.02% |
| EDC1412 | 2 | 0.02% |
| EDC2458 | 2 | 0.02% |
| EDC0508 | 2 | 0.02% |
| EDC2648 | 2 | 0.02% |
| EDC2706 | 2 | 0.02% |
| EDC3452 | 2 | 0.02% |
| EDC5213 | 2 | 0.02% |
| EDC5191 | 2 | 0.02% |
| EDC4784 | 2 | 0.02% |
| EDC0525 | 2 | 0.02% |
| EDC1520 | 2 | 0.02% |
| EDC3721 | 2 | 0.02% |
| EDC1659 | 2 | 0.02% |
| EDC3920 | 2 | 0.02% |
| EDC0429 | 2 | 0.02% |
| EDC2485 | 2 | 0.02% |
| EDC4539 | 2 | 0.02% |
| EDC0083 | 2 | 0.02% |
| EDC1486 | 2 | 0.02% |
| EDC3492 | 2 | 0.02% |
| EDC3460 | 2 | 0.02% |
| EDC1954 | 2 | 0.02% |
| EDC4744 | 2 | 0.02% |
| EDC5320 | 2 | 0.02% |
| EDC4897 | 2 | 0.02% |
| EDC1764 | 2 | 0.02% |
| EDC2740 | 2 | 0.02% |
| EDC3645 | 2 | 0.02% |
| EDC2382 | 2 | 0.02% |
| EDC3047 | 2 | 0.02% |
| EDC2632 | 2 | 0.02% |
| EDC3296 | 2 | 0.02% |
| EDC0995 | 2 | 0.02% |
| EDC2820 | 2 | 0.02% |
| EDC2305 | 2 | 0.02% |
| EDC0074 | 2 | 0.02% |
| EDC2687 | 2 | 0.02% |
| EDC3399 | 2 | 0.02% |
| EDC3053 | 2 | 0.02% |
| EDC2169 | 2 | 0.02% |
| EDC4224 | 2 | 0.02% |
| EDC1181 | 2 | 0.02% |
| EDC4367 | 2 | 0.02% |
| EDC3350 | 2 | 0.02% |
| EDC5112 | 2 | 0.02% |
| EDC5269 | 2 | 0.02% |
| EDC1135 | 2 | 0.02% |
| EDC2540 | 2 | 0.02% |
| EDC0504 | 2 | 0.02% |
| EDC4670 | 2 | 0.02% |
| EDC1164 | 2 | 0.02% |
| EDC0391 | 2 | 0.02% |
| EDC3322 | 2 | 0.02% |
| EDC0331 | 2 | 0.02% |
| EDC0109 | 2 | 0.02% |
| EDC3988 | 2 | 0.02% |
| EDC0586 | 2 | 0.02% |
| EDC1881 | 2 | 0.02% |
| EDC5233 | 2 | 0.02% |
| EDC1072 | 2 | 0.02% |
| EDC1111 | 2 | 0.02% |
| EDC0349 | 2 | 0.02% |
| EDC0173 | 2 | 0.02% |
| EDC2374 | 2 | 0.02% |
| EDC1456 | 2 | 0.02% |
| EDC4348 | 2 | 0.02% |
| EDC4142 | 2 | 0.02% |
| EDC1948 | 2 | 0.02% |
| EDC1966 | 2 | 0.02% |
| EDC3677 | 2 | 0.02% |
| EDC0744 | 2 | 0.02% |
| EDC0722 | 2 | 0.02% |
| EDC3087 | 2 | 0.02% |
| EDC0649 | 2 | 0.02% |
| EDC0259 | 2 | 0.02% |
| EDC2017 | 2 | 0.02% |
| EDC4886 | 2 | 0.02% |
| EDC3187 | 2 | 0.02% |
| EDC2272 | 2 | 0.02% |
| EDC3946 | 2 | 0.02% |
| EDC1946 | 2 | 0.02% |
| EDC3391 | 2 | 0.02% |
| EDC5290 | 2 | 0.02% |
| EDC4992 | 2 | 0.02% |
| EDC4277 | 2 | 0.02% |
| EDC1655 | 2 | 0.02% |
| EDC1211 | 2 | 0.02% |
| EDC0449 | 2 | 0.02% |
| EDC1747 | 2 | 0.02% |
| EDC2318 | 2 | 0.02% |
| EDC3142 | 2 | 0.02% |
| EDC4485 | 2 | 0.02% |
| EDC3540 | 2 | 0.02% |
| EDC2089 | 2 | 0.02% |
| EDC5144 | 2 | 0.02% |
| EDC3205 | 2 | 0.02% |
| EDC5020 | 2 | 0.02% |
| EDC1427 | 2 | 0.02% |
| EDC3954 | 2 | 0.02% |
| EDC4542 | 2 | 0.02% |
| EDC3024 | 2 | 0.02% |
| EDC3539 | 2 | 0.02% |
| EDC2353 | 2 | 0.02% |
| EDC1352 | 2 | 0.02% |
| EDC3752 | 2 | 0.02% |
| EDC4602 | 2 | 0.02% |
| EDC3100 | 2 | 0.02% |
| EDC0280 | 2 | 0.02% |
| EDC2133 | 2 | 0.02% |
| EDC4518 | 2 | 0.02% |
| EDC4226 | 2 | 0.02% |
| EDC1131 | 2 | 0.02% |
| EDC0603 | 2 | 0.02% |
| EDC1299 | 2 | 0.02% |
| EDC2829 | 2 | 0.02% |
| EDC2008 | 2 | 0.02% |
| EDC0710 | 2 | 0.02% |
| EDC2263 | 2 | 0.02% |
| EDC0162 | 2 | 0.02% |
| EDC4800 | 2 | 0.02% |
| EDC4722 | 2 | 0.02% |
| EDC0610 | 2 | 0.02% |
| EDC0131 | 2 | 0.02% |
| EDC5040 | 2 | 0.02% |
| EDC3175 | 2 | 0.02% |
| EDC4217 | 2 | 0.02% |
| EDC1617 | 2 | 0.02% |
| EDC5332 | 2 | 0.02% |
| EDC4139 | 2 | 0.02% |
| EDC2346 | 2 | 0.02% |
| EDC1097 | 2 | 0.02% |
| EDC2170 | 2 | 0.02% |
| EDC5159 | 2 | 0.02% |
| EDC2320 | 2 | 0.02% |
| EDC1090 | 2 | 0.02% |
| EDC4293 | 2 | 0.02% |
| EDC3816 | 2 | 0.02% |
| EDC4865 | 2 | 0.02% |
| EDC2160 | 2 | 0.02% |
| EDC2946 | 2 | 0.02% |
| EDC3071 | 2 | 0.02% |
| EDC0698 | 2 | 0.02% |
| EDC2268 | 2 | 0.02% |
| EDC2503 | 2 | 0.02% |
| EDC1217 | 2 | 0.02% |
| EDC2370 | 2 | 0.02% |
| EDC2729 | 2 | 0.02% |
| EDC2654 | 2 | 0.02% |
| EDC3176 | 2 | 0.02% |
| EDC2564 | 2 | 0.02% |
| EDC3905 | 2 | 0.02% |
| EDC0176 | 2 | 0.02% |
| EDC2587 | 2 | 0.02% |
| EDC2578 | 2 | 0.02% |
| EDC2960 | 2 | 0.02% |
| EDC0054 | 2 | 0.02% |
| EDC1176 | 2 | 0.02% |
| EDC2518 | 2 | 0.02% |
| EDC2744 | 2 | 0.02% |
| EDC4691 | 2 | 0.02% |
| EDC1348 | 2 | 0.02% |
| EDC1540 | 2 | 0.02% |
| EDC0725 | 2 | 0.02% |
| EDC2942 | 2 | 0.02% |
| EDC5124 | 2 | 0.02% |
| EDC4632 | 2 | 0.02% |
| EDC0540 | 2 | 0.02% |
| EDC5005 | 2 | 0.02% |
| EDC0388 | 2 | 0.02% |
| EDC1916 | 2 | 0.02% |
| EDC5041 | 2 | 0.02% |
| EDC2784 | 2 | 0.02% |
| EDC3365 | 2 | 0.02% |
| EDC4215 | 2 | 0.02% |
| EDC3698 | 2 | 0.02% |
| EDC3566 | 2 | 0.02% |
| EDC0170 | 2 | 0.02% |
| EDC5114 | 2 | 0.02% |
| EDC3583 | 2 | 0.02% |
| EDC0888 | 2 | 0.02% |
| EDC4779 | 2 | 0.02% |
| EDC3259 | 2 | 0.02% |
| EDC2840 | 2 | 0.02% |
| EDC4707 | 2 | 0.02% |
| EDC3293 | 2 | 0.02% |
| EDC0156 | 2 | 0.02% |
| EDC4544 | 2 | 0.02% |
| EDC5116 | 2 | 0.02% |
| EDC0495 | 2 | 0.02% |
| EDC2861 | 2 | 0.02% |
| EDC3076 | 2 | 0.02% |
| EDC2344 | 2 | 0.02% |
| EDC2197 | 2 | 0.02% |
| EDC2388 | 2 | 0.02% |
| EDC4881 | 2 | 0.02% |
| EDC1897 | 2 | 0.02% |
| EDC1367 | 2 | 0.02% |
| EDC0939 | 2 | 0.02% |
| EDC4870 | 2 | 0.02% |
| EDC0741 | 2 | 0.02% |
| EDC1700 | 2 | 0.02% |
| EDC3191 | 2 | 0.02% |
| EDC2273 | 2 | 0.02% |
| EDC3662 | 2 | 0.02% |
| EDC1605 | 2 | 0.02% |
| EDC1834 | 2 | 0.02% |
| EDC1886 | 2 | 0.02% |
| EDC0679 | 2 | 0.02% |
| EDC4558 | 2 | 0.02% |
| EDC5284 | 2 | 0.02% |
| EDC5207 | 2 | 0.02% |
| EDC0501 | 2 | 0.02% |
| EDC0881 | 2 | 0.02% |
| EDC3727 | 2 | 0.02% |
| EDC4154 | 2 | 0.02% |
| EDC4698 | 2 | 0.02% |
| EDC4623 | 2 | 0.02% |
| EDC3687 | 2 | 0.02% |
| EDC4478 | 2 | 0.02% |
| EDC0016 | 2 | 0.02% |
| EDC2757 | 2 | 0.02% |
| EDC4248 | 2 | 0.02% |
| EDC5310 | 2 | 0.02% |
| EDC1716 | 2 | 0.02% |
| EDC1988 | 2 | 0.02% |
| EDC0823 | 2 | 0.02% |
| EDC3023 | 2 | 0.02% |
| EDC3949 | 2 | 0.02% |
| EDC3431 | 2 | 0.02% |
| EDC3203 | 2 | 0.02% |
| EDC5133 | 2 | 0.02% |
| EDC5142 | 2 | 0.02% |
| EDC4425 | 2 | 0.02% |
| EDC3450 | 2 | 0.02% |
| EDC1482 | 2 | 0.02% |
| EDC4645 | 2 | 0.02% |
| EDC2130 | 2 | 0.02% |
| EDC0794 | 2 | 0.02% |
| EDC1062 | 2 | 0.02% |
| EDC1067 | 2 | 0.02% |
| EDC2323 | 2 | 0.02% |
| EDC5254 | 2 | 0.02% |
| EDC1885 | 2 | 0.02% |
| EDC1078 | 2 | 0.02% |
| EDC0324 | 2 | 0.02% |
| EDC1906 | 2 | 0.02% |
| EDC4996 | 2 | 0.02% |
| EDC1051 | 2 | 0.02% |
| EDC2776 | 2 | 0.02% |
| EDC3328 | 2 | 0.02% |
| EDC1260 | 2 | 0.02% |
| EDC0114 | 2 | 0.02% |
| EDC4620 | 2 | 0.02% |
| EDC4876 | 2 | 0.02% |
| EDC3886 | 2 | 0.02% |
| EDC5064 | 2 | 0.02% |
| EDC0505 | 2 | 0.02% |
| EDC2975 | 2 | 0.02% |
| EDC1353 | 2 | 0.02% |
| EDC3000 | 2 | 0.02% |
| EDC0453 | 2 | 0.02% |
| EDC3774 | 2 | 0.02% |
| EDC4280 | 2 | 0.02% |
| EDC2860 | 2 | 0.02% |
| EDC3851 | 2 | 0.02% |
| EDC3561 | 2 | 0.02% |
| EDC2007 | 2 | 0.02% |
| EDC2247 | 2 | 0.02% |
| EDC0317 | 2 | 0.02% |
| EDC2362 | 2 | 0.02% |
| EDC1604 | 2 | 0.02% |
| EDC3177 | 2 | 0.02% |
| EDC1726 | 2 | 0.02% |
| EDC4129 | 2 | 0.02% |
| EDC3817 | 2 | 0.02% |
| EDC1745 | 2 | 0.02% |
| EDC4250 | 2 | 0.02% |
| EDC4236 | 2 | 0.02% |
| EDC2407 | 2 | 0.02% |
| EDC0745 | 2 | 0.02% |
| EDC4830 | 2 | 0.02% |
| EDC5007 | 2 | 0.02% |
| EDC4043 | 2 | 0.02% |
| EDC4719 | 2 | 0.02% |
| EDC0945 | 2 | 0.02% |
| EDC4616 | 2 | 0.02% |
| EDC0799 | 2 | 0.02% |
| EDC0909 | 2 | 0.02% |
| EDC3879 | 2 | 0.02% |
| EDC5160 | 2 | 0.02% |
| EDC3793 | 2 | 0.02% |
| EDC2986 | 2 | 0.02% |
| EDC2905 | 2 | 0.02% |
| EDC0684 | 2 | 0.02% |
| EDC1781 | 2 | 0.02% |
| EDC0409 | 2 | 0.02% |
| EDC2399 | 2 | 0.02% |
| EDC5317 | 2 | 0.02% |
| EDC5246 | 2 | 0.02% |
| EDC2718 | 2 | 0.02% |
| EDC5258 | 2 | 0.02% |
| EDC5002 | 2 | 0.02% |
| EDC4024 | 2 | 0.02% |
| EDC1435 | 2 | 0.02% |
| EDC1628 | 2 | 0.02% |
| EDC5248 | 2 | 0.02% |
| EDC2085 | 2 | 0.02% |
| EDC2512 | 2 | 0.02% |
| EDC0108 | 2 | 0.02% |
| EDC1735 | 2 | 0.02% |
| EDC4940 | 2 | 0.02% |
| EDC3519 | 2 | 0.02% |
| EDC5181 | 2 | 0.02% |
| EDC0855 | 2 | 0.02% |
| EDC2000 | 2 | 0.02% |
| EDC0625 | 2 | 0.02% |
| EDC1979 | 2 | 0.02% |
| EDC4482 | 2 | 0.02% |
| EDC0334 | 2 | 0.02% |
| EDC5189 | 2 | 0.02% |
| EDC4296 | 2 | 0.02% |
| EDC1762 | 2 | 0.02% |
| EDC0934 | 2 | 0.02% |
| EDC0551 | 2 | 0.02% |
| EDC3997 | 2 | 0.02% |
| EDC4203 | 2 | 0.02% |
| EDC4287 | 2 | 0.02% |
| EDC4149 | 2 | 0.02% |
| EDC2736 | 2 | 0.02% |
| EDC0135 | 2 | 0.02% |
| EDC4274 | 2 | 0.02% |
| EDC2548 | 2 | 0.02% |
| EDC3813 | 2 | 0.02% |
| EDC3279 | 2 | 0.02% |
| EDC1121 | 2 | 0.02% |
| EDC3239 | 2 | 0.02% |
| EDC5217 | 2 | 0.02% |
| EDC4776 | 2 | 0.02% |
| EDC0840 | 2 | 0.02% |
| EDC4687 | 2 | 0.02% |
| EDC1882 | 2 | 0.02% |
| EDC5267 | 2 | 0.02% |
| EDC4974 | 2 | 0.02% |
| EDC4889 | 2 | 0.02% |
| EDC1088 | 2 | 0.02% |
| EDC0953 | 2 | 0.02% |
| EDC0803 | 2 | 0.02% |
| EDC1721 | 2 | 0.02% |
| EDC3819 | 2 | 0.02% |
| EDC4655 | 2 | 0.02% |
| EDC4268 | 2 | 0.02% |
| EDC2114 | 2 | 0.02% |
| EDC2832 | 2 | 0.02% |
| EDC2207 | 2 | 0.02% |
| EDC2092 | 2 | 0.02% |
| EDC2134 | 2 | 0.02% |
| EDC3654 | 2 | 0.02% |
| EDC3941 | 2 | 0.02% |
| EDC1048 | 2 | 0.02% |
| EDC2129 | 2 | 0.02% |
| EDC1996 | 2 | 0.02% |
| EDC2754 | 2 | 0.02% |
| EDC1089 | 2 | 0.02% |
| EDC5048 | 2 | 0.02% |
| EDC1810 | 2 | 0.02% |
| EDC2938 | 2 | 0.02% |
| EDC3107 | 2 | 0.02% |
| EDC3608 | 2 | 0.02% |
| EDC3596 | 2 | 0.02% |
| EDC0362 | 2 | 0.02% |
| EDC4253 | 2 | 0.02% |
| EDC4042 | 2 | 0.02% |
| EDC1307 | 2 | 0.02% |
| EDC0489 | 2 | 0.02% |
| EDC2570 | 2 | 0.02% |
| EDC4256 | 2 | 0.02% |
| EDC1007 | 2 | 0.02% |
| EDC4281 | 2 | 0.02% |
| EDC2522 | 2 | 0.02% |
| EDC3246 | 2 | 0.02% |
| EDC0771 | 2 | 0.02% |
| EDC3758 | 2 | 0.02% |
| EDC1422 | 2 | 0.02% |
| EDC4164 | 2 | 0.02% |
| EDC4460 | 2 | 0.02% |
| EDC0691 | 2 | 0.02% |
| EDC2918 | 2 | 0.02% |
| EDC3049 | 2 | 0.02% |
| EDC4964 | 2 | 0.02% |
| EDC2026 | 2 | 0.02% |
| EDC4354 | 2 | 0.02% |
| EDC0801 | 2 | 0.02% |
| EDC2386 | 2 | 0.02% |
| EDC5032 | 2 | 0.02% |
| EDC4590 | 2 | 0.02% |
| EDC0230 | 2 | 0.02% |
| EDC4300 | 2 | 0.02% |
| EDC2922 | 2 | 0.02% |
| EDC0188 | 2 | 0.02% |
| EDC2955 | 2 | 0.02% |
| EDC4562 | 2 | 0.02% |
| EDC3679 | 2 | 0.02% |
| EDC5109 | 2 | 0.02% |
| EDC3830 | 2 | 0.02% |
| EDC4021 | 2 | 0.02% |
| EDC3385 | 2 | 0.02% |
| EDC4181 | 2 | 0.02% |
| EDC2676 | 2 | 0.02% |
| EDC3464 | 2 | 0.02% |
| EDC2728 | 2 | 0.02% |
| EDC1366 | 2 | 0.02% |
| EDC4505 | 2 | 0.02% |
| EDC3579 | 2 | 0.02% |
| EDC3894 | 2 | 0.02% |
| EDC5026 | 2 | 0.02% |
| EDC4220 | 2 | 0.02% |
| EDC4507 | 2 | 0.02% |
| EDC1004 | 2 | 0.02% |
| EDC3806 | 2 | 0.02% |
| EDC4797 | 2 | 0.02% |
| EDC1006 | 2 | 0.02% |
| EDC5095 | 2 | 0.02% |
| EDC2980 | 2 | 0.02% |
| EDC4245 | 2 | 0.02% |
| EDC2510 | 2 | 0.02% |
| EDC3008 | 2 | 0.02% |
| EDC1068 | 2 | 0.02% |
| EDC3225 | 2 | 0.02% |
| EDC3530 | 2 | 0.02% |
| EDC1235 | 2 | 0.02% |
| EDC0335 | 2 | 0.02% |
| EDC4407 | 2 | 0.02% |
| EDC2619 | 2 | 0.02% |
| EDC2125 | 2 | 0.02% |
| EDC0724 | 2 | 0.02% |
| EDC4449 | 2 | 0.02% |
| EDC3473 | 2 | 0.02% |
| EDC5194 | 2 | 0.02% |
| EDC5009 | 2 | 0.02% |
| EDC4398 | 2 | 0.02% |
| EDC0422 | 2 | 0.02% |
| EDC0588 | 2 | 0.02% |
| EDC3099 | 2 | 0.02% |
| EDC1974 | 2 | 0.02% |
| EDC2623 | 2 | 0.02% |
| EDC2717 | 2 | 0.02% |
| EDC4935 | 2 | 0.02% |
| EDC1201 | 2 | 0.02% |
| EDC4132 | 2 | 0.02% |
| EDC2420 | 2 | 0.02% |
| EDC1129 | 2 | 0.02% |
| EDC0254 | 2 | 0.02% |
| EDC4934 | 2 | 0.02% |
| EDC4328 | 2 | 0.02% |
| EDC2563 | 2 | 0.02% |
| EDC1807 | 2 | 0.02% |
| EDC1889 | 2 | 0.02% |
| EDC4882 | 2 | 0.02% |
| EDC4196 | 2 | 0.02% |
| EDC4140 | 2 | 0.02% |
| EDC4438 | 2 | 0.02% |
| EDC4570 | 2 | 0.02% |
| EDC1232 | 2 | 0.02% |
| EDC0819 | 2 | 0.02% |
| EDC3930 | 2 | 0.02% |
| EDC0342 | 2 | 0.02% |
| EDC3700 | 2 | 0.02% |
| EDC1765 | 2 | 0.02% |
| EDC2910 | 2 | 0.02% |
| EDC0682 | 2 | 0.02% |
| EDC1632 | 2 | 0.02% |
| EDC4251 | 2 | 0.02% |
| EDC4735 | 2 | 0.02% |
| EDC3432 | 2 | 0.02% |
| EDC1857 | 2 | 0.02% |
| EDC1083 | 2 | 0.02% |
| EDC2896 | 2 | 0.02% |
| EDC4306 | 2 | 0.02% |
| EDC2639 | 2 | 0.02% |
| EDC2775 | 2 | 0.02% |
| EDC5211 | 2 | 0.02% |
| EDC1019 | 2 | 0.02% |
| EDC3896 | 2 | 0.02% |
| EDC1827 | 2 | 0.02% |
| EDC2872 | 2 | 0.02% |
| EDC5296 | 2 | 0.02% |
| EDC4950 | 2 | 0.02% |
| EDC3256 | 2 | 0.02% |
| EDC5149 | 2 | 0.02% |
| EDC0415 | 2 | 0.02% |
| EDC3068 | 2 | 0.02% |
| EDC0886 | 2 | 0.02% |
| EDC1289 | 2 | 0.02% |
| EDC0177 | 2 | 0.02% |
| EDC4598 | 2 | 0.02% |
| EDC2565 | 2 | 0.02% |
| EDC1503 | 2 | 0.02% |
| EDC1110 | 2 | 0.02% |
| EDC0377 | 2 | 0.02% |
| EDC1293 | 2 | 0.02% |
| EDC2148 | 2 | 0.02% |
| EDC1555 | 2 | 0.02% |
| EDC4316 | 2 | 0.02% |
| EDC2822 | 2 | 0.02% |
| EDC1799 | 2 | 0.02% |
| EDC0444 | 2 | 0.02% |
| EDC2581 | 2 | 0.02% |
| EDC4611 | 2 | 0.02% |
| EDC3857 | 2 | 0.02% |
| EDC2692 | 2 | 0.02% |
| EDC0907 | 2 | 0.02% |
| EDC3831 | 2 | 0.02% |
| EDC3728 | 2 | 0.02% |
| EDC4917 | 2 | 0.02% |
| EDC2083 | 2 | 0.02% |
| EDC2685 | 2 | 0.02% |
| EDC1576 | 2 | 0.02% |
| EDC2449 | 2 | 0.02% |
| EDC4076 | 2 | 0.02% |
| EDC0158 | 2 | 0.02% |
| EDC0152 | 2 | 0.02% |
| EDC0928 | 2 | 0.02% |
| EDC3526 | 2 | 0.02% |
| EDC2404 | 2 | 0.02% |
| EDC1952 | 2 | 0.02% |
| EDC0553 | 2 | 0.02% |
| EDC0894 | 2 | 0.02% |
| EDC5062 | 2 | 0.02% |
| EDC3213 | 2 | 0.02% |
| EDC3083 | 2 | 0.02% |
| EDC3935 | 2 | 0.02% |
| EDC4047 | 2 | 0.02% |
| EDC2806 | 2 | 0.02% |
| EDC2212 | 2 | 0.02% |
| EDC2396 | 2 | 0.02% |
| EDC0180 | 2 | 0.02% |
| EDC3141 | 2 | 0.02% |
| EDC3636 | 2 | 0.02% |
| EDC2818 | 2 | 0.02% |
| EDC0462 | 2 | 0.02% |
| EDC1574 | 2 | 0.02% |
| EDC1451 | 2 | 0.02% |
| EDC0264 | 2 | 0.02% |
| EDC3926 | 2 | 0.02% |
| EDC4855 | 2 | 0.02% |
| EDC2966 | 2 | 0.02% |
| EDC4456 | 2 | 0.02% |
| EDC2241 | 2 | 0.02% |
| EDC1470 | 2 | 0.02% |
| EDC2331 | 2 | 0.02% |
| EDC3021 | 2 | 0.02% |
| EDC4943 | 2 | 0.02% |
| EDC4720 | 2 | 0.02% |
| EDC1666 | 2 | 0.02% |
| EDC0913 | 2 | 0.02% |
| EDC4273 | 2 | 0.02% |
| EDC1921 | 2 | 0.02% |
| EDC4049 | 2 | 0.02% |
| EDC4214 | 2 | 0.02% |
| EDC2054 | 2 | 0.02% |
| EDC2493 | 2 | 0.02% |
| EDC1627 | 2 | 0.02% |
| EDC4466 | 2 | 0.02% |
| EDC2541 | 2 | 0.02% |
| EDC3631 | 2 | 0.02% |
| EDC2974 | 2 | 0.02% |
| EDC3080 | 2 | 0.02% |
| EDC4186 | 2 | 0.02% |
| EDC0933 | 2 | 0.02% |
| EDC1290 | 2 | 0.02% |
| EDC0026 | 2 | 0.02% |
| EDC3346 | 2 | 0.02% |
| EDC1209 | 2 | 0.02% |
| EDC2575 | 2 | 0.02% |
| EDC4872 | 2 | 0.02% |
| EDC3972 | 2 | 0.02% |
| EDC3897 | 2 | 0.02% |
| EDC3867 | 2 | 0.02% |
| EDC2141 | 2 | 0.02% |
| EDC0091 | 2 | 0.02% |
| EDC0889 | 2 | 0.02% |
| EDC1524 | 2 | 0.02% |
| EDC2855 | 2 | 0.02% |
| EDC1925 | 2 | 0.02% |
| EDC3747 | 2 | 0.02% |
| EDC2841 | 2 | 0.02% |
| EDC5193 | 2 | 0.02% |
| EDC2308 | 2 | 0.02% |
| EDC3343 | 2 | 0.02% |
| EDC1334 | 2 | 0.02% |
| EDC2154 | 2 | 0.02% |
| EDC1247 | 2 | 0.02% |
| EDC1609 | 2 | 0.02% |
| EDC0319 | 2 | 0.02% |
| EDC2231 | 2 | 0.02% |
| EDC2742 | 2 | 0.02% |
| EDC1805 | 2 | 0.02% |
| EDC0475 | 2 | 0.02% |
| EDC2155 | 2 | 0.02% |
| EDC5166 | 2 | 0.02% |
| EDC2392 | 2 | 0.02% |
| EDC5240 | 2 | 0.02% |
| EDC2082 | 2 | 0.02% |
| EDC1835 | 2 | 0.02% |
| EDC1502 | 2 | 0.02% |
| EDC0828 | 2 | 0.02% |
| EDC4231 | 2 | 0.02% |
| EDC1575 | 2 | 0.02% |
| EDC0884 | 2 | 0.02% |
| EDC1619 | 2 | 0.02% |
| EDC3034 | 2 | 0.02% |
| EDC0365 | 2 | 0.02% |
| EDC3123 | 2 | 0.02% |
| EDC2788 | 2 | 0.02% |
| EDC4772 | 2 | 0.02% |
| EDC3305 | 2 | 0.02% |
| EDC0845 | 2 | 0.02% |
| EDC4487 | 2 | 0.02% |
| EDC5017 | 2 | 0.02% |
| EDC4862 | 2 | 0.02% |
| EDC1651 | 2 | 0.02% |
| EDC4795 | 2 | 0.02% |
| EDC4883 | 2 | 0.02% |
| EDC2669 | 2 | 0.02% |
| EDC0893 | 2 | 0.02% |
| EDC3401 | 2 | 0.02% |
| EDC3581 | 2 | 0.02% |
| EDC2280 | 2 | 0.02% |
| EDC1377 | 2 | 0.02% |
| EDC4428 | 2 | 0.02% |
| EDC3607 | 2 | 0.02% |
| EDC2877 | 2 | 0.02% |
| EDC0518 | 2 | 0.02% |
| EDC2440 | 2 | 0.02% |
| EDC3715 | 2 | 0.02% |
| EDC4004 | 2 | 0.02% |
| EDC4919 | 2 | 0.02% |
| EDC2653 | 2 | 0.02% |
| EDC3750 | 2 | 0.02% |
| EDC3680 | 2 | 0.02% |
| EDC3828 | 2 | 0.02% |
| EDC2852 | 2 | 0.02% |
| EDC4493 | 2 | 0.02% |
| EDC0086 | 2 | 0.02% |
| EDC2254 | 2 | 0.02% |
| EDC2302 | 2 | 0.02% |
| EDC1616 | 2 | 0.02% |
| EDC2100 | 2 | 0.02% |
| EDC1064 | 2 | 0.02% |
| EDC3822 | 2 | 0.02% |
| EDC4682 | 2 | 0.02% |
| EDC0071 | 2 | 0.02% |
| EDC2730 | 2 | 0.02% |
| EDC1968 | 2 | 0.02% |
| EDC4786 | 2 | 0.02% |
| EDC3329 | 2 | 0.02% |
| EDC0631 | 2 | 0.02% |
| EDC1476 | 2 | 0.02% |
| EDC2310 | 2 | 0.02% |
| EDC2175 | 2 | 0.02% |
| EDC3386 | 2 | 0.02% |
| EDC5285 | 2 | 0.02% |
| EDC0678 | 2 | 0.02% |
| EDC1783 | 2 | 0.02% |
| EDC4932 | 2 | 0.02% |
| EDC0027 | 2 | 0.02% |
| EDC4467 | 2 | 0.02% |
| EDC3521 | 2 | 0.02% |
| EDC2078 | 2 | 0.02% |
| EDC4775 | 2 | 0.02% |
| EDC0457 | 2 | 0.02% |
| EDC0739 | 2 | 0.02% |
| EDC0675 | 2 | 0.02% |
| EDC4748 | 2 | 0.02% |
| EDC3321 | 2 | 0.02% |
| EDC1577 | 2 | 0.02% |
| EDC3353 | 2 | 0.02% |
| EDC4053 | 2 | 0.02% |
| EDC0778 | 2 | 0.02% |
| EDC5004 | 2 | 0.02% |
| EDC1757 | 2 | 0.02% |
| EDC0367 | 2 | 0.02% |
| EDC5093 | 2 | 0.02% |
| EDC4079 | 2 | 0.02% |
| EDC2194 | 2 | 0.02% |
| EDC0060 | 2 | 0.02% |
| EDC3344 | 2 | 0.02% |
| EDC1000 | 2 | 0.02% |
| EDC2069 | 2 | 0.02% |
| EDC2713 | 2 | 0.02% |
| EDC1296 | 2 | 0.02% |
| EDC1803 | 2 | 0.02% |
| EDC2065 | 2 | 0.02% |
| EDC4504 | 2 | 0.02% |
| EDC2041 | 2 | 0.02% |
| EDC2022 | 2 | 0.02% |
| EDC4208 | 2 | 0.02% |
| EDC1626 | 2 | 0.02% |
| EDC0010 | 2 | 0.02% |
| EDC4583 | 2 | 0.02% |
| EDC4762 | 2 | 0.02% |
| EDC3234 | 2 | 0.02% |
| EDC3939 | 2 | 0.02% |
| EDC0628 | 2 | 0.02% |
| EDC0095 | 2 | 0.02% |
| EDC3649 | 2 | 0.02% |
| EDC3873 | 2 | 0.02% |
| EDC1449 | 2 | 0.02% |
| EDC1297 | 2 | 0.02% |
| EDC5132 | 2 | 0.02% |
| EDC2183 | 2 | 0.02% |
| EDC4147 | 2 | 0.02% |
| EDC3064 | 2 | 0.02% |
| EDC0614 | 2 | 0.02% |
| EDC1769 | 2 | 0.02% |
| EDC3255 | 2 | 0.02% |
| EDC4759 | 2 | 0.02% |
| EDC2878 | 2 | 0.02% |
| EDC0650 | 2 | 0.02% |
| EDC3724 | 2 | 0.02% |
| EDC4963 | 2 | 0.02% |
| EDC2915 | 2 | 0.02% |
| EDC0228 | 2 | 0.02% |
| EDC0404 | 2 | 0.02% |
| EDC0275 | 2 | 0.02% |
| EDC2959 | 2 | 0.02% |
| EDC2609 | 2 | 0.02% |
| EDC1318 | 2 | 0.02% |
| EDC3751 | 2 | 0.02% |
| EDC1038 | 2 | 0.02% |
| EDC4364 | 2 | 0.02% |
| EDC3829 | 2 | 0.02% |
| EDC1237 | 2 | 0.02% |
| EDC2870 | 2 | 0.02% |
| EDC2173 | 2 | 0.02% |
| EDC0235 | 2 | 0.02% |
| EDC0602 | 2 | 0.02% |
| EDC3575 | 2 | 0.02% |
| EDC0303 | 2 | 0.02% |
| EDC5308 | 2 | 0.02% |
| EDC0706 | 2 | 0.02% |
| EDC4223 | 2 | 0.02% |
| EDC3243 | 2 | 0.02% |
| EDC2081 | 2 | 0.02% |
| EDC2984 | 2 | 0.02% |
| EDC1713 | 2 | 0.02% |
| EDC4685 | 2 | 0.02% |
| EDC5011 | 2 | 0.02% |
| EDC3318 | 2 | 0.02% |
| EDC0666 | 2 | 0.02% |
| EDC5131 | 2 | 0.02% |
| EDC4568 | 2 | 0.02% |
| EDC0270 | 2 | 0.02% |
| EDC1527 | 2 | 0.02% |
| EDC0121 | 2 | 0.02% |
| EDC3228 | 2 | 0.02% |
| EDC3520 | 2 | 0.02% |
| EDC1326 | 2 | 0.02% |
| EDC2743 | 2 | 0.02% |
| EDC4011 | 2 | 0.02% |
| EDC1794 | 2 | 0.02% |
| EDC4853 | 2 | 0.02% |
| EDC3096 | 2 | 0.02% |
| EDC4279 | 2 | 0.02% |
| EDC2965 | 2 | 0.02% |
| EDC2481 | 2 | 0.02% |
| EDC1283 | 2 | 0.02% |
| EDC1434 | 2 | 0.02% |
| EDC3430 | 2 | 0.02% |
| EDC0104 | 2 | 0.02% |
| EDC3493 | 2 | 0.02% |
| EDC1887 | 2 | 0.02% |
| EDC4327 | 2 | 0.02% |
| EDC1265 | 2 | 0.02% |
| EDC1250 | 2 | 0.02% |
| EDC2643 | 2 | 0.02% |
| EDC1106 | 2 | 0.02% |
| EDC5059 | 2 | 0.02% |
| EDC2174 | 2 | 0.02% |
| EDC0100 | 2 | 0.02% |
| EDC4829 | 2 | 0.02% |
| EDC0172 | 2 | 0.02% |
| EDC0982 | 2 | 0.02% |
| EDC1519 | 2 | 0.02% |
| EDC0219 | 2 | 0.02% |
| EDC5107 | 2 | 0.02% |
| EDC5331 | 2 | 0.02% |
| EDC0103 | 2 | 0.02% |
| EDC0381 | 2 | 0.02% |
| EDC0879 | 2 | 0.02% |
| EDC0880 | 2 | 0.02% |
| EDC2983 | 2 | 0.02% |
| EDC1790 | 2 | 0.02% |
| EDC1304 | 2 | 0.02% |
| EDC1356 | 2 | 0.02% |
| EDC2891 | 2 | 0.02% |
| EDC3529 | 2 | 0.02% |
| EDC2145 | 2 | 0.02% |
| EDC0285 | 2 | 0.02% |
| EDC1720 | 2 | 0.02% |
| EDC3127 | 2 | 0.02% |
| EDC5265 | 2 | 0.02% |
| EDC2415 | 2 | 0.02% |
| EDC3441 | 2 | 0.02% |
| EDC0621 | 2 | 0.02% |
| EDC4188 | 2 | 0.02% |
| EDC1467 | 2 | 0.02% |
| EDC3950 | 2 | 0.02% |
| EDC2403 | 2 | 0.02% |
| EDC0454 | 2 | 0.02% |
| EDC4442 | 2 | 0.02% |
| EDC1919 | 2 | 0.02% |
| EDC0320 | 2 | 0.02% |
| EDC0672 | 2 | 0.02% |
| EDC0786 | 2 | 0.02% |
| EDC3633 | 2 | 0.02% |
| EDC3523 | 2 | 0.02% |
| EDC1506 | 2 | 0.02% |
| EDC3788 | 2 | 0.02% |
| EDC0047 | 2 | 0.02% |
| EDC1864 | 2 | 0.02% |
| EDC4900 | 2 | 0.02% |
| EDC1468 | 2 | 0.02% |
| EDC0791 | 2 | 0.02% |
| EDC0904 | 2 | 0.02% |
| EDC2636 | 2 | 0.02% |
| EDC0902 | 2 | 0.02% |
| EDC0978 | 2 | 0.02% |
| EDC3839 | 2 | 0.02% |
| EDC0416 | 2 | 0.02% |
| EDC2441 | 2 | 0.02% |
| EDC3639 | 2 | 0.02% |
| EDC1723 | 2 | 0.02% |
| EDC3039 | 2 | 0.02% |
| EDC3340 | 2 | 0.02% |
| EDC5237 | 2 | 0.02% |
| EDC0921 | 2 | 0.02% |
| EDC2926 | 2 | 0.02% |
| EDC0477 | 2 | 0.02% |
| EDC3995 | 2 | 0.02% |
| EDC4283 | 2 | 0.02% |
| EDC4322 | 2 | 0.02% |
| EDC0046 | 2 | 0.02% |
| EDC1047 | 2 | 0.02% |
| EDC5243 | 2 | 0.02% |
| EDC3220 | 2 | 0.02% |
| EDC0809 | 2 | 0.02% |
| EDC4643 | 2 | 0.02% |
| EDC0700 | 2 | 0.02% |
| EDC0810 | 2 | 0.02% |
| EDC2122 | 2 | 0.02% |
| EDC0009 | 2 | 0.02% |
| EDC4235 | 2 | 0.02% |
| EDC4033 | 2 | 0.02% |
| EDC0033 | 2 | 0.02% |
| EDC0611 | 2 | 0.02% |
| EDC2224 | 2 | 0.02% |
| EDC4078 | 2 | 0.02% |
| EDC2661 | 2 | 0.02% |
| EDC1262 | 2 | 0.02% |
| EDC0825 | 2 | 0.02% |
| EDC3197 | 2 | 0.02% |
| EDC1119 | 2 | 0.02% |
| EDC1101 | 2 | 0.02% |
| EDC4027 | 2 | 0.02% |
| EDC1374 | 2 | 0.02% |
| EDC2647 | 2 | 0.02% |
| EDC4587 | 2 | 0.02% |
| EDC2640 | 2 | 0.02% |
| EDC3574 | 2 | 0.02% |
| EDC1261 | 2 | 0.02% |
| EDC0310 | 2 | 0.02% |
| EDC1953 | 2 | 0.02% |
| EDC1724 | 2 | 0.02% |
| EDC1985 | 2 | 0.02% |
| EDC2751 | 2 | 0.02% |
| EDC1499 | 2 | 0.02% |
| EDC3762 | 2 | 0.02% |
| EDC1218 | 2 | 0.02% |
| EDC5303 | 2 | 0.02% |
| EDC1817 | 2 | 0.02% |
| EDC3265 | 2 | 0.02% |
| EDC0290 | 2 | 0.02% |
| EDC2631 | 2 | 0.02% |
| EDC4136 | 2 | 0.02% |
| EDC2711 | 2 | 0.02% |
| EDC1612 | 2 | 0.02% |
| EDC4179 | 2 | 0.02% |
| EDC5203 | 2 | 0.02% |
| EDC1729 | 2 | 0.02% |
| EDC2218 | 2 | 0.02% |
| EDC2734 | 2 | 0.02% |
| EDC3748 | 2 | 0.02% |
| EDC1965 | 2 | 0.02% |
| EDC0816 | 2 | 0.02% |
| EDC3669 | 2 | 0.02% |
| EDC2159 | 2 | 0.02% |
| EDC3577 | 2 | 0.02% |
| EDC4314 | 2 | 0.02% |
| EDC3606 | 2 | 0.02% |
| EDC0692 | 2 | 0.02% |
| EDC1339 | 2 | 0.02% |
| EDC1058 | 2 | 0.02% |
| EDC3066 | 2 | 0.02% |
| EDC3101 | 2 | 0.02% |
| EDC4476 | 2 | 0.02% |
| EDC5334 | 2 | 0.02% |
| EDC0035 | 2 | 0.02% |
| EDC2634 | 2 | 0.02% |
| EDC5306 | 2 | 0.02% |
| EDC4895 | 2 | 0.02% |
| EDC4888 | 2 | 0.02% |
| EDC2814 | 2 | 0.02% |
| EDC4956 | 2 | 0.02% |
| EDC4584 | 2 | 0.02% |
| EDC4988 | 2 | 0.02% |
| EDC1999 | 2 | 0.02% |
| EDC0062 | 2 | 0.02% |
| EDC4593 | 2 | 0.02% |
| EDC4678 | 2 | 0.02% |
| EDC0730 | 2 | 0.02% |
| EDC2793 | 2 | 0.02% |
| EDC1956 | 2 | 0.02% |
| EDC4921 | 2 | 0.02% |
| EDC1960 | 2 | 0.02% |
| EDC2127 | 2 | 0.02% |
| EDC3168 | 2 | 0.02% |
| EDC0443 | 2 | 0.02% |
| EDC2932 | 2 | 0.02% |
| EDC5309 | 2 | 0.02% |
| EDC0598 | 2 | 0.02% |
| EDC1357 | 2 | 0.02% |
| EDC0107 | 2 | 0.02% |
| EDC3069 | 2 | 0.02% |
| EDC4332 | 2 | 0.02% |
| EDC3684 | 2 | 0.02% |
| EDC5263 | 2 | 0.02% |
| EDC0435 | 2 | 0.02% |
| EDC0096 | 2 | 0.02% |
| EDC3011 | 2 | 0.02% |
| EDC0750 | 2 | 0.02% |
| EDC2760 | 2 | 0.02% |
| EDC0028 | 2 | 0.02% |
| EDC1324 | 2 | 0.02% |
| EDC0182 | 2 | 0.02% |
| EDC3125 | 1 | 0.01% |
| EDC1831 | 1 | 0.01% |
| EDC4810 | 1 | 0.01% |
| EDC4110 | 1 | 0.01% |
| EDC0486 | 1 | 0.01% |
| EDC2229 | 1 | 0.01% |
| EDC4547 | 1 | 0.01% |
| EDC2704 | 1 | 0.01% |
| EDC2614 | 1 | 0.01% |
| EDC4997 | 1 | 0.01% |
| EDC2028 | 1 | 0.01% |
| EDC1255 | 1 | 0.01% |
| EDC3202 | 1 | 0.01% |
| EDC2073 | 1 | 0.01% |
| EDC4432 | 1 | 0.01% |
| EDC0196 | 1 | 0.01% |
| EDC0497 | 1 | 0.01% |
| EDC4174 | 1 | 0.01% |
| EDC0917 | 1 | 0.01% |
| EDC0468 | 1 | 0.01% |
| EDC4982 | 1 | 0.01% |
| EDC3342 | 1 | 0.01% |
| EDC3635 | 1 | 0.01% |
| EDC2033 | 1 | 0.01% |
| EDC5126 | 1 | 0.01% |
| EDC2633 | 1 | 0.01% |
| EDC0524 | 1 | 0.01% |
| EDC4574 | 1 | 0.01% |
| EDC3709 | 1 | 0.01% |
| EDC3874 | 1 | 0.01% |
| EDC5184 | 1 | 0.01% |
| EDC4978 | 1 | 0.01% |
| EDC3148 | 1 | 0.01% |
| EDC4706 | 1 | 0.01% |
| EDC4094 | 1 | 0.01% |
| EDC3678 | 1 | 0.01% |
| EDC3307 | 1 | 0.01% |
| EDC0703 | 1 | 0.01% |
| EDC1584 | 1 | 0.01% |
| EDC2461 | 1 | 0.01% |
| EDC3979 | 1 | 0.01% |
| EDC0102 | 1 | 0.01% |
| EDC4391 | 1 | 0.01% |
| EDC1521 | 1 | 0.01% |
| EDC4911 | 1 | 0.01% |
| EDC4899 | 1 | 0.01% |
| EDC3102 | 1 | 0.01% |
| EDC0811 | 1 | 0.01% |
| EDC0250 | 1 | 0.01% |
| EDC2232 | 1 | 0.01% |
| EDC2871 | 1 | 0.01% |
| EDC4014 | 1 | 0.01% |
| EDC4941 | 1 | 0.01% |
| EDC5136 | 1 | 0.01% |
| EDC3113 | 1 | 0.01% |
| EDC4201 | 1 | 0.01% |
| EDC3231 | 1 | 0.01% |
| EDC1899 | 1 | 0.01% |
| EDC1157 | 1 | 0.01% |
| EDC2867 | 1 | 0.01% |
| EDC1998 | 1 | 0.01% |
| EDC3928 | 1 | 0.01% |
| EDC0268 | 1 | 0.01% |
| EDC4040 | 1 | 0.01% |
| EDC5252 | 1 | 0.01% |
| EDC5204 | 1 | 0.01% |
| EDC3974 | 1 | 0.01% |
| EDC2315 | 1 | 0.01% |
| EDC2764 | 1 | 0.01% |
| EDC2709 | 1 | 0.01% |
| EDC4433 | 1 | 0.01% |
| EDC4843 | 1 | 0.01% |
| EDC0642 | 1 | 0.01% |
| EDC0661 | 1 | 0.01% |
| EDC2997 | 1 | 0.01% |
| EDC2698 | 1 | 0.01% |
| EDC2892 | 1 | 0.01% |
| EDC4626 | 1 | 0.01% |
| EDC0012 | 1 | 0.01% |
| EDC1178 | 1 | 0.01% |
| EDC4177 | 1 | 0.01% |
| EDC5015 | 1 | 0.01% |
| EDC2424 | 1 | 0.01% |
| EDC2859 | 1 | 0.01% |
| EDC3286 | 1 | 0.01% |
| EDC4044 | 1 | 0.01% |
| EDC0938 | 1 | 0.01% |
| EDC4726 | 1 | 0.01% |
| EDC2917 | 1 | 0.01% |
| EDC3393 | 1 | 0.01% |
| EDC4297 | 1 | 0.01% |
| EDC4723 | 1 | 0.01% |
| EDC1611 | 1 | 0.01% |
| EDC0390 | 1 | 0.01% |
| EDC2070 | 1 | 0.01% |
| EDC0850 | 1 | 0.01% |
| EDC5083 | 1 | 0.01% |
| EDC1321 | 1 | 0.01% |
| EDC1513 | 1 | 0.01% |
| EDC0503 | 1 | 0.01% |
| EDC4477 | 1 | 0.01% |
| EDC2269 | 1 | 0.01% |
| EDC0787 | 1 | 0.01% |
| EDC4820 | 1 | 0.01% |
| EDC2152 | 1 | 0.01% |
| EDC3778 | 1 | 0.01% |
| EDC2656 | 1 | 0.01% |
| EDC4108 | 1 | 0.01% |
| EDC1194 | 1 | 0.01% |
| EDC3825 | 1 | 0.01% |
| EDC0535 | 1 | 0.01% |
| EDC3746 | 1 | 0.01% |
| EDC5054 | 1 | 0.01% |
| EDC3288 | 1 | 0.01% |
| EDC2116 | 1 | 0.01% |
| EDC0892 | 1 | 0.01% |
| EDC5324 | 1 | 0.01% |
| EDC1225 | 1 | 0.01% |
| EDC5295 | 1 | 0.01% |
| EDC0037 | 1 | 0.01% |
| EDC5028 | 1 | 0.01% |
| EDC3295 | 1 | 0.01% |
| EDC0673 | 1 | 0.01% |
| EDC0169 | 1 | 0.01% |
| EDC1838 | 1 | 0.01% |
| EDC3403 | 1 | 0.01% |
| EDC2501 | 1 | 0.01% |
| EDC2360 | 1 | 0.01% |
| EDC3019 | 1 | 0.01% |
| EDC1955 | 1 | 0.01% |
| EDC3798 | 1 | 0.01% |
| EDC2234 | 1 | 0.01% |
| EDC0885 | 1 | 0.01% |
| EDC1141 | 1 | 0.01% |
| EDC5223 | 1 | 0.01% |
| EDC1480 | 1 | 0.01% |
| EDC3249 | 1 | 0.01% |
| EDC3313 | 1 | 0.01% |
| EDC5209 | 1 | 0.01% |
| EDC4740 | 1 | 0.01% |
| EDC4157 | 1 | 0.01% |
| EDC0713 | 1 | 0.01% |
| EDC4912 | 1 | 0.01% |
| EDC0820 | 1 | 0.01% |
| EDC1539 | 1 | 0.01% |
| EDC1675 | 1 | 0.01% |
| EDC0901 | 1 | 0.01% |
| EDC1849 | 1 | 0.01% |
| EDC2497 | 1 | 0.01% |
| EDC5079 | 1 | 0.01% |
| EDC0199 | 1 | 0.01% |
| EDC5055 | 1 | 0.01% |
| EDC3629 | 1 | 0.01% |
| EDC4733 | 1 | 0.01% |
| EDC4637 | 1 | 0.01% |
| EDC1937 | 1 | 0.01% |
| EDC5195 | 1 | 0.01% |
| EDC2294 | 1 | 0.01% |
| EDC0493 | 1 | 0.01% |
| EDC0148 | 1 | 0.01% |
| EDC2185 | 1 | 0.01% |
| EDC4101 | 1 | 0.01% |
| EDC5190 | 1 | 0.01% |
| EDC4793 | 1 | 0.01% |
| EDC3652 | 1 | 0.01% |
| EDC3089 | 1 | 0.01% |
| EDC3597 | 1 | 0.01% |
| EDC3195 | 1 | 0.01% |
| EDC4543 | 1 | 0.01% |
| EDC5085 | 1 | 0.01% |
| EDC1478 | 1 | 0.01% |
| EDC4608 | 1 | 0.01% |
| EDC3769 | 1 | 0.01% |
| EDC0677 | 1 | 0.01% |
| EDC4847 | 1 | 0.01% |
| EDC3358 | 1 | 0.01% |
| EDC3560 | 1 | 0.01% |
| EDC3708 | 1 | 0.01% |
| EDC4892 | 1 | 0.01% |
| EDC4471 | 1 | 0.01% |
| EDC1818 | 1 | 0.01% |
| EDC4690 | 1 | 0.01% |
| EDC1071 | 1 | 0.01% |
| EDC3925 | 1 | 0.01% |
| EDC0167 | 1 | 0.01% |
| EDC2221 | 1 | 0.01% |
| EDC0950 | 1 | 0.01% |
| EDC2789 | 1 | 0.01% |
| EDC1665 | 1 | 0.01% |
| EDC3499 | 1 | 0.01% |
| EDC2957 | 1 | 0.01% |
| EDC1657 | 1 | 0.01% |
| EDC1206 | 1 | 0.01% |
| EDC3689 | 1 | 0.01% |
| EDC4304 | 1 | 0.01% |
| EDC0962 | 1 | 0.01% |
| EDC1743 | 1 | 0.01% |
| EDC0795 | 1 | 0.01% |
| EDC3598 | 1 | 0.01% |
| EDC3467 | 1 | 0.01% |
| EDC4420 | 1 | 0.01% |
| EDC4995 | 1 | 0.01% |
| EDC5221 | 1 | 0.01% |
| EDC3435 | 1 | 0.01% |
| EDC1316 | 1 | 0.01% |
| EDC0124 | 1 | 0.01% |
| EDC3411 | 1 | 0.01% |
| EDC1516 | 1 | 0.01% |
| EDC0082 | 1 | 0.01% |
| EDC2707 | 1 | 0.01% |
| EDC3510 | 1 | 0.01% |
| EDC2956 | 1 | 0.01% |
| EDC4893 | 1 | 0.01% |
| EDC0203 | 1 | 0.01% |
| EDC4500 | 1 | 0.01% |
| EDC2151 | 1 | 0.01% |
| EDC1733 | 1 | 0.01% |
| EDC5171 | 1 | 0.01% |
| EDC4440 | 1 | 0.01% |
| EDC1498 | 1 | 0.01% |
| EDC2115 | 1 | 0.01% |
| EDC4661 | 1 | 0.01% |
| EDC1719 | 1 | 0.01% |
| EDC1568 | 1 | 0.01% |
| EDC4252 | 1 | 0.01% |
| EDC4365 | 1 | 0.01% |
| EDC2499 | 1 | 0.01% |
| EDC0105 | 1 | 0.01% |
| EDC4506 | 1 | 0.01% |
| EDC1829 | 1 | 0.01% |
| EDC2355 | 1 | 0.01% |
| EDC4143 | 1 | 0.01% |
| EDC4103 | 1 | 0.01% |
| EDC0363 | 1 | 0.01% |
| EDC4497 | 1 | 0.01% |
| EDC4003 | 1 | 0.01% |
| EDC4405 | 1 | 0.01% |
| EDC2579 | 1 | 0.01% |
| EDC2284 | 1 | 0.01% |
| EDC3932 | 1 | 0.01% |
| EDC3498 | 1 | 0.01% |
| EDC0442 | 1 | 0.01% |
| EDC0993 | 1 | 0.01% |
| EDC0315 | 1 | 0.01% |
| EDC4894 | 1 | 0.01% |
| EDC2948 | 1 | 0.01% |
| EDC4341 | 1 | 0.01% |
| EDC2723 | 1 | 0.01% |
| EDC4050 | 1 | 0.01% |
| EDC3807 | 1 | 0.01% |
| EDC4699 | 1 | 0.01% |
| EDC0123 | 1 | 0.01% |
| EDC1066 | 1 | 0.01% |
| EDC0240 | 1 | 0.01% |
| EDC1020 | 1 | 0.01% |
| EDC4993 | 1 | 0.01% |
| EDC4114 | 1 | 0.01% |
| EDC3153 | 1 | 0.01% |
| EDC0312 | 1 | 0.01% |
| EDC5241 | 1 | 0.01% |
| EDC0101 | 1 | 0.01% |
| EDC5018 | 1 | 0.01% |
| EDC2937 | 1 | 0.01% |
| EDC4840 | 1 | 0.01% |
| EDC4981 | 1 | 0.01% |
| EDC4624 | 1 | 0.01% |
| EDC2801 | 1 | 0.01% |
| EDC0966 | 1 | 0.01% |
| EDC4884 | 1 | 0.01% |
| EDC3392 | 1 | 0.01% |
| EDC1445 | 1 | 0.01% |
| EDC2052 | 1 | 0.01% |
| EDC0683 | 1 | 0.01% |
| EDC3805 | 1 | 0.01% |
| EDC1847 | 1 | 0.01% |
| EDC3193 | 1 | 0.01% |
| EDC2105 | 1 | 0.01% |
| EDC0397 | 1 | 0.01% |
| EDC4832 | 1 | 0.01% |
| EDC4066 | 1 | 0.01% |
| EDC1752 | 1 | 0.01% |
| EDC4663 | 1 | 0.01% |
| EDC2236 | 1 | 0.01% |
| EDC4187 | 1 | 0.01% |
| EDC5068 | 1 | 0.01% |
| EDC4794 | 1 | 0.01% |
| EDC5033 | 1 | 0.01% |
| EDC4400 | 1 | 0.01% |
| EDC1277 | 1 | 0.01% |
| EDC3206 | 1 | 0.01% |
| EDC5162 | 1 | 0.01% |
| EDC4694 | 1 | 0.01% |
| EDC3971 | 1 | 0.01% |
| EDC4095 | 1 | 0.01% |
| EDC2397 | 1 | 0.01% |
| EDC1600 | 1 | 0.01% |
| EDC4710 | 1 | 0.01% |
| EDC4168 | 1 | 0.01% |
| EDC4414 | 1 | 0.01% |
| EDC4359 | 1 | 0.01% |
| EDC2591 | 1 | 0.01% |
| EDC3491 | 1 | 0.01% |
| EDC0045 | 1 | 0.01% |
| EDC0136 | 1 | 0.01% |
| EDC4173 | 1 | 0.01% |
| EDC0146 | 1 | 0.01% |
| EDC2712 | 1 | 0.01% |
| EDC1229 | 1 | 0.01% |
| EDC2246 | 1 | 0.01% |
| EDC3868 | 1 | 0.01% |
| EDC2699 | 1 | 0.01% |
| EDC2602 | 1 | 0.01% |
| EDC5323 | 1 | 0.01% |
| EDC2553 | 1 | 0.01% |
| EDC2118 | 1 | 0.01% |
| EDC4046 | 1 | 0.01% |
| EDC0548 | 1 | 0.01% |
| EDC4436 | 1 | 0.01% |
| EDC3165 | 1 | 0.01% |
| EDC4563 | 1 | 0.01% |
| EDC3132 | 1 | 0.01% |
| EDC2351 | 1 | 0.01% |
| EDC3477 | 1 | 0.01% |
| EDC1635 | 1 | 0.01% |
| EDC1096 | 1 | 0.01% |
| EDC4376 | 1 | 0.01% |
| EDC4059 | 1 | 0.01% |
| EDC0920 | 1 | 0.01% |
| EDC1368 | 1 | 0.01% |
| EDC2733 | 1 | 0.01% |
| EDC4153 | 1 | 0.01% |
| EDC5255 | 1 | 0.01% |
| EDC5313 | 1 | 0.01% |
| EDC1399 | 1 | 0.01% |
| EDC4854 | 1 | 0.01% |
| EDC1796 | 1 | 0.01% |
| EDC3557 | 1 | 0.01% |
| EDC4990 | 1 | 0.01% |
| EDC4565 | 1 | 0.01% |
| EDC4350 | 1 | 0.01% |
| EDC2035 | 1 | 0.01% |
| EDC3046 | 1 | 0.01% |
| EDC1678 | 1 | 0.01% |
| EDC1686 | 1 | 0.01% |
| EDC3506 | 1 | 0.01% |
| EDC2401 | 1 | 0.01% |
| EDC3916 | 1 | 0.01% |
| EDC2790 | 1 | 0.01% |
| EDC4349 | 1 | 0.01% |
| EDC1703 | 1 | 0.01% |
| EDC1875 | 1 | 0.01% |
| EDC1291 | 1 | 0.01% |
| EDC3860 | 1 | 0.01% |
| EDC3378 | 1 | 0.01% |
| EDC3130 | 1 | 0.01% |
| EDC0704 | 1 | 0.01% |
| EDC1914 | 1 | 0.01% |
| EDC4097 | 1 | 0.01% |
| EDC1722 | 1 | 0.01% |
| EDC0857 | 1 | 0.01% |
| EDC2621 | 1 | 0.01% |
| EDC3371 | 1 | 0.01% |
| EDC4267 | 1 | 0.01% |
| EDC3466 | 1 | 0.01% |
| EDC3112 | 1 | 0.01% |
| EDC3164 | 1 | 0.01% |
| EDC2472 | 1 | 0.01% |
| EDC5174 | 1 | 0.01% |
| EDC0712 | 1 | 0.01% |
| EDC2048 | 1 | 0.01% |
| EDC3870 | 1 | 0.01% |
| EDC1742 | 1 | 0.01% |
| EDC3211 | 1 | 0.01% |
| EDC1483 | 1 | 0.01% |
| EDC1122 | 1 | 0.01% |
| EDC2876 | 1 | 0.01% |
| EDC4700 | 1 | 0.01% |
| EDC5063 | 1 | 0.01% |
| EDC3404 | 1 | 0.01% |
| EDC2238 | 1 | 0.01% |
| EDC4823 | 1 | 0.01% |
| EDC2630 | 1 | 0.01% |
| EDC2006 | 1 | 0.01% |
| EDC2843 | 1 | 0.01% |
| EDC0163 | 1 | 0.01% |
| EDC2258 | 1 | 0.01% |
| EDC4709 | 1 | 0.01% |
| EDC1531 | 1 | 0.01% |
| EDC0450 | 1 | 0.01% |
| EDC0766 | 1 | 0.01% |
| EDC0542 | 1 | 0.01% |
| EDC1935 | 1 | 0.01% |
| EDC3710 | 1 | 0.01% |
| EDC5165 | 1 | 0.01% |
| EDC3973 | 1 | 0.01% |
| EDC2529 | 1 | 0.01% |
| EDC4545 | 1 | 0.01% |
| EDC3060 | 1 | 0.01% |
| EDC2766 | 1 | 0.01% |
| EDC3012 | 1 | 0.01% |
| EDC1384 | 1 | 0.01% |
| EDC3844 | 1 | 0.01% |
| EDC2837 | 1 | 0.01% |
| EDC1682 | 1 | 0.01% |
| EDC0522 | 1 | 0.01% |
| EDC0561 | 1 | 0.01% |
| EDC4617 | 1 | 0.01% |
| EDC3892 | 1 | 0.01% |
| EDC3052 | 1 | 0.01% |
| EDC2275 | 1 | 0.01% |
| EDC5006 | 1 | 0.01% |
| EDC1363 | 1 | 0.01% |
| EDC2875 | 1 | 0.01% |
| EDC2240 | 1 | 0.01% |
| EDC3418 | 1 | 0.01% |
| EDC2786 | 1 | 0.01% |
| EDC3093 | 1 | 0.01% |
| EDC1959 | 1 | 0.01% |
| EDC5228 | 1 | 0.01% |
| EDC3705 | 1 | 0.01% |
| EDC3790 | 1 | 0.01% |
| EDC0831 | 1 | 0.01% |
| EDC0008 | 1 | 0.01% |
| EDC0864 | 1 | 0.01% |
| EDC4092 | 1 | 0.01% |
| EDC3274 | 1 | 0.01% |
| EDC1530 | 1 | 0.01% |
| EDC1683 | 1 | 0.01% |
| EDC1010 | 1 | 0.01% |
| EDC0687 | 1 | 0.01% |
| EDC2446 | 1 | 0.01% |
| EDC3846 | 1 | 0.01% |
| EDC4999 | 1 | 0.01% |
| EDC1438 | 1 | 0.01% |
| EDC2482 | 1 | 0.01% |
| EDC0981 | 1 | 0.01% |
| EDC1776 | 1 | 0.01% |
| EDC0511 | 1 | 0.01% |
| EDC1967 | 1 | 0.01% |
| EDC3884 | 1 | 0.01% |
| EDC2800 | 1 | 0.01% |
| EDC3181 | 1 | 0.01% |
| EDC3531 | 1 | 0.01% |
| EDC1687 | 1 | 0.01% |
| EDC3300 | 1 | 0.01% |
| EDC2423 | 1 | 0.01% |
| EDC1360 | 1 | 0.01% |
| EDC0185 | 1 | 0.01% |
| EDC3962 | 1 | 0.01% |
| EDC4770 | 1 | 0.01% |
| EDC4715 | 1 | 0.01% |
| EDC2002 | 1 | 0.01% |
| EDC4065 | 1 | 0.01% |
| EDC0159 | 1 | 0.01% |
| EDC0407 | 1 | 0.01% |
| EDC0281 | 1 | 0.01% |
| EDC0601 | 1 | 0.01% |
| EDC1779 | 1 | 0.01% |
| EDC0157 | 1 | 0.01% |
| EDC3660 | 1 | 0.01% |
| EDC0742 | 1 | 0.01% |
| EDC0197 | 1 | 0.01% |
| EDC1692 | 1 | 0.01% |
| EDC2967 | 1 | 0.01% |
| EDC1802 | 1 | 0.01% |
| EDC5319 | 1 | 0.01% |
| EDC3948 | 1 | 0.01% |
| EDC0164 | 1 | 0.01% |
| EDC0371 | 1 | 0.01% |
| EDC1572 | 1 | 0.01% |
| EDC4469 | 1 | 0.01% |
| EDC4357 | 1 | 0.01% |
| EDC5276 | 1 | 0.01% |
| EDC3908 | 1 | 0.01% |
| EDC0500 | 1 | 0.01% |
| EDC2195 | 1 | 0.01% |
| EDC4010 | 1 | 0.01% |
| EDC3174 | 1 | 0.01% |
| EDC0313 | 1 | 0.01% |
| EDC3588 | 1 | 0.01% |
| EDC0289 | 1 | 0.01% |
| EDC4966 | 1 | 0.01% |
| EDC4156 | 1 | 0.01% |
| EDC3951 | 1 | 0.01% |
| EDC0641 | 1 | 0.01% |
| EDC2453 | 1 | 0.01% |
| EDC4117 | 1 | 0.01% |
| EDC3235 | 1 | 0.01% |
| EDC2819 | 1 | 0.01% |
| EDC0021 | 1 | 0.01% |
| EDC1436 | 1 | 0.01% |
| EDC2416 | 1 | 0.01% |
| EDC1044 | 1 | 0.01% |
| EDC3504 | 1 | 0.01% |
| EDC0865 | 1 | 0.01% |
| EDC0660 | 1 | 0.01% |
| EDC3799 | 1 | 0.01% |
| EDC1898 | 1 | 0.01% |
| EDC2363 | 1 | 0.01% |
| EDC4987 | 1 | 0.01% |
| EDC1443 | 1 | 0.01% |
| EDC4451 | 1 | 0.01% |
| EDC0458 | 1 | 0.01% |
| EDC4338 | 1 | 0.01% |
| EDC3146 | 1 | 0.01% |
| EDC2598 | 1 | 0.01% |
| EDC4648 | 1 | 0.01% |
| EDC4986 | 1 | 0.01% |
| EDC3308 | 1 | 0.01% |
| EDC4439 | 1 | 0.01% |
| EDC1054 | 1 | 0.01% |
| EDC4005 | 1 | 0.01% |
| EDC0726 | 1 | 0.01% |
| EDC5105 | 1 | 0.01% |
| EDC0291 | 1 | 0.01% |
| EDC0585 | 1 | 0.01% |
| EDC3192 | 1 | 0.01% |
| EDC1251 | 1 | 0.01% |
| EDC0615 | 1 | 0.01% |
| EDC1345 | 1 | 0.01% |
| EDC0242 | 1 | 0.01% |
| EDC2543 | 1 | 0.01% |
| EDC3869 | 1 | 0.01% |
| EDC4124 | 1 | 0.01% |
| EDC0402 | 1 | 0.01% |
| EDC4970 | 1 | 0.01% |
| EDC3266 | 1 | 0.01% |
| EDC5330 | 1 | 0.01% |
| EDC4942 | 1 | 0.01% |
| EDC3824 | 1 | 0.01% |
| EDC2921 | 1 | 0.01% |
| EDC4490 | 1 | 0.01% |
| EDC0646 | 1 | 0.01% |
| EDC4785 | 1 | 0.01% |
| EDC3956 | 1 | 0.01% |
| EDC4734 | 1 | 0.01% |
| EDC1378 | 1 | 0.01% |
| EDC2053 | 1 | 0.01% |
| EDC0212 | 1 | 0.01% |
| EDC2888 | 1 | 0.01% |
| EDC4531 | 1 | 0.01% |
| EDC4692 | 1 | 0.01% |
| EDC0272 | 1 | 0.01% |
| EDC1039 | 1 | 0.01% |
| EDC5014 | 1 | 0.01% |
| EDC1767 | 1 | 0.01% |
| EDC4555 | 1 | 0.01% |
| EDC0674 | 1 | 0.01% |
| EDC4827 | 1 | 0.01% |
| EDC3906 | 1 | 0.01% |
| EDC4169 | 1 | 0.01% |
| EDC0368 | 1 | 0.01% |
| EDC2597 | 1 | 0.01% |
| EDC3207 | 1 | 0.01% |
| EDC2809 | 1 | 0.01% |
| EDC2873 | 1 | 0.01% |
| EDC3015 | 1 | 0.01% |
| EDC5238 | 1 | 0.01% |
| EDC1536 | 1 | 0.01% |
| EDC4308 | 1 | 0.01% |
| EDC3166 | 1 | 0.01% |
| EDC2590 | 1 | 0.01% |
| EDC0232 | 1 | 0.01% |
| EDC1311 | 1 | 0.01% |
| EDC2715 | 1 | 0.01% |
| EDC3301 | 1 | 0.01% |
| EDC4262 | 1 | 0.01% |
| EDC3094 | 1 | 0.01% |
| EDC2196 | 1 | 0.01% |
| EDC4554 | 1 | 0.01% |
| EDC1782 | 1 | 0.01% |
| EDC0225 | 1 | 0.01% |
| EDC2193 | 1 | 0.01% |
| EDC1964 | 1 | 0.01% |
| EDC3676 | 1 | 0.01% |
| EDC2726 | 1 | 0.01% |
| EDC2762 | 1 | 0.01% |
| EDC1582 | 1 | 0.01% |
| EDC1073 | 1 | 0.01% |
| EDC2051 | 1 | 0.01% |
| EDC2090 | 1 | 0.01% |
| EDC2516 | 1 | 0.01% |
| EDC4641 | 1 | 0.01% |
| EDC2939 | 1 | 0.01% |
| EDC4930 | 1 | 0.01% |
| EDC3699 | 1 | 0.01% |
| EDC2874 | 1 | 0.01% |
| EDC1598 | 1 | 0.01% |
| EDC4977 | 1 | 0.01% |
| EDC2186 | 1 | 0.01% |
| EDC1870 | 1 | 0.01% |
| EDC5261 | 1 | 0.01% |
| EDC4549 | 1 | 0.01% |
| EDC3270 | 1 | 0.01% |
| EDC1607 | 1 | 0.01% |
| EDC3370 | 1 | 0.01% |
| EDC1132 | 1 | 0.01% |
| EDC2450 | 1 | 0.01% |
| EDC3287 | 1 | 0.01% |
| EDC2316 | 1 | 0.01% |
| EDC1710 | 1 | 0.01% |
| EDC1544 | 1 | 0.01% |
| EDC4633 | 1 | 0.01% |
| EDC2725 | 1 | 0.01% |
| EDC2903 | 1 | 0.01% |
| EDC5086 | 1 | 0.01% |
| EDC4321 | 1 | 0.01% |
| EDC4344 | 1 | 0.01% |
| EDC4515 | 1 | 0.01% |
| EDC0043 | 1 | 0.01% |
| EDC2245 | 1 | 0.01% |
| EDC3952 | 1 | 0.01% |
| EDC3443 | 1 | 0.01% |
| EDC1172 | 1 | 0.01% |
| EDC2489 | 1 | 0.01% |
| EDC4789 | 1 | 0.01% |
| EDC3161 | 1 | 0.01% |
| EDC4294 | 1 | 0.01% |
| EDC0800 | 1 | 0.01% |
| EDC1061 | 1 | 0.01% |
| EDC4596 | 1 | 0.01% |
| EDC3254 | 1 | 0.01% |
| EDC1458 | 1 | 0.01% |
| EDC0912 | 1 | 0.01% |
| EDC0491 | 1 | 0.01% |
| EDC5147 | 1 | 0.01% |
| EDC4517 | 1 | 0.01% |
| EDC1618 | 1 | 0.01% |
| EDC0419 | 1 | 0.01% |
| EDC5080 | 1 | 0.01% |
| EDC2190 | 1 | 0.01% |
| EDC2383 | 1 | 0.01% |
| EDC3427 | 1 | 0.01% |
| EDC1380 | 1 | 0.01% |
| EDC3571 | 1 | 0.01% |
| EDC5247 | 1 | 0.01% |
| EDC4989 | 1 | 0.01% |
| EDC0029 | 1 | 0.01% |
| EDC3242 | 1 | 0.01% |
| EDC2778 | 1 | 0.01% |
| EDC3408 | 1 | 0.01% |
| EDC0896 | 1 | 0.01% |
| EDC0835 | 1 | 0.01% |
| EDC1278 | 1 | 0.01% |
| EDC3217 | 1 | 0.01% |
| EDC0116 | 1 | 0.01% |
| EDC5186 | 1 | 0.01% |
| EDC1929 | 1 | 0.01% |
| EDC0651 | 1 | 0.01% |
| EDC0106 | 1 | 0.01% |
| EDC4546 | 1 | 0.01% |
| EDC3338 | 1 | 0.01% |
| EDC0627 | 1 | 0.01% |
| EDC2844 | 1 | 0.01% |
| EDC3138 | 1 | 0.01% |
| EDC3672 | 1 | 0.01% |
| EDC2697 | 1 | 0.01% |
| EDC0784 | 1 | 0.01% |
| EDC2616 | 1 | 0.01% |
| EDC1517 | 1 | 0.01% |
| EDC0059 | 1 | 0.01% |
| EDC4654 | 1 | 0.01% |
| EDC0681 | 1 | 0.01% |
| EDC2520 | 1 | 0.01% |
| EDC1117 | 1 | 0.01% |
| EDC0355 | 1 | 0.01% |
| EDC5337 | 1 | 0.01% |
| EDC2414 | 1 | 0.01% |
| EDC4331 | 1 | 0.01% |
| EDC0788 | 1 | 0.01% |
| EDC4453 | 1 | 0.01% |
| EDC2644 | 1 | 0.01% |
| EDC2136 | 1 | 0.01% |
| EDC5060 | 1 | 0.01% |
| EDC3314 | 1 | 0.01% |
| EDC3106 | 1 | 0.01% |
| EDC4973 | 1 | 0.01% |
| EDC3226 | 1 | 0.01% |
| EDC3465 | 1 | 0.01% |
| EDC0247 | 1 | 0.01% |
| EDC5176 | 1 | 0.01% |
| EDC1633 | 1 | 0.01% |
| EDC4443 | 1 | 0.01% |
| EDC3183 | 1 | 0.01% |
| EDC3188 | 1 | 0.01% |
| EDC5222 | 1 | 0.01% |
| EDC0092 | 1 | 0.01% |
| EDC3665 | 1 | 0.01% |
| EDC1493 | 1 | 0.01% |
| EDC0882 | 1 | 0.01% |
| EDC0695 | 1 | 0.01% |
| EDC2817 | 1 | 0.01% |
| EDC2945 | 1 | 0.01% |
| EDC1789 | 1 | 0.01% |
| EDC3109 | 1 | 0.01% |
| EDC3159 | 1 | 0.01% |
| EDC0014 | 1 | 0.01% |
| EDC2339 | 1 | 0.01% |
| EDC0908 | 1 | 0.01% |
| EDC4533 | 1 | 0.01% |
| EDC1173 | 1 | 0.01% |
| EDC1428 | 1 | 0.01% |
| EDC4269 | 1 | 0.01% |
| EDC3694 | 1 | 0.01% |
| EDC2761 | 1 | 0.01% |
| EDC0676 | 1 | 0.01% |
| EDC2328 | 1 | 0.01% |
| EDC3104 | 1 | 0.01% |
| EDC2312 | 1 | 0.01% |
| EDC2267 | 1 | 0.01% |
| EDC3092 | 1 | 0.01% |
| EDC3086 | 1 | 0.01% |
| EDC0736 | 1 | 0.01% |
| EDC4319 | 1 | 0.01% |
| EDC4479 | 1 | 0.01% |
| EDC3349 | 1 | 0.01% |
| EDC1139 | 1 | 0.01% |
| EDC3944 | 1 | 0.01% |
| EDC3871 | 1 | 0.01% |
| EDC5328 | 1 | 0.01% |
| EDC1059 | 1 | 0.01% |
| EDC0251 | 1 | 0.01% |
| EDC0428 | 1 | 0.01% |
| EDC4971 | 1 | 0.01% |
| EDC4096 | 1 | 0.01% |
| EDC2206 | 1 | 0.01% |
| EDC4383 | 1 | 0.01% |
| EDC4569 | 1 | 0.01% |
| EDC2452 | 1 | 0.01% |
| EDC0412 | 1 | 0.01% |
| EDC2361 | 1 | 0.01% |
| EDC1033 | 1 | 0.01% |
| EDC3323 | 1 | 0.01% |
| EDC1730 | 1 | 0.01% |
| EDC1210 | 1 | 0.01% |
| EDC5117 | 1 | 0.01% |
| EDC4753 | 1 | 0.01% |
| EDC0134 | 1 | 0.01% |
| EDC0436 | 1 | 0.01% |
| EDC3686 | 1 | 0.01% |
| EDC3812 | 1 | 0.01% |
| EDC0897 | 1 | 0.01% |
| EDC1319 | 1 | 0.01% |
| EDC3924 | 1 | 0.01% |
| EDC1242 | 1 | 0.01% |
| EDC4237 | 1 | 0.01% |
| EDC3081 | 1 | 0.01% |
| EDC0746 | 1 | 0.01% |
| EDC0070 | 1 | 0.01% |
| EDC4098 | 1 | 0.01% |
| EDC1522 | 1 | 0.01% |
| EDC4841 | 1 | 0.01% |
| EDC1962 | 1 | 0.01% |
| EDC0424 | 1 | 0.01% |
| EDC4198 | 1 | 0.01% |
| EDC1631 | 1 | 0.01% |
| EDC0604 | 1 | 0.01% |
| EDC0479 | 1 | 0.01% |
| EDC0476 | 1 | 0.01% |
| EDC1204 | 1 | 0.01% |
| EDC4381 | 1 | 0.01% |
| EDC0241 | 1 | 0.01% |
| EDC2468 | 1 | 0.01% |
| EDC1874 | 1 | 0.01% |
| EDC5103 | 1 | 0.01% |
| EDC5150 | 1 | 0.01% |
| EDC4086 | 1 | 0.01% |
| EDC0150 | 1 | 0.01% |
| EDC1273 | 1 | 0.01% |
| EDC2810 | 1 | 0.01% |
| EDC0140 | 1 | 0.01% |
| EDC1673 | 1 | 0.01% |
| EDC5151 | 1 | 0.01% |
| EDC3667 | 1 | 0.01% |
| EDC3704 | 1 | 0.01% |
| EDC0464 | 1 | 0.01% |
| EDC4783 | 1 | 0.01% |
| EDC4060 | 1 | 0.01% |
| EDC3887 | 1 | 0.01% |
| EDC4525 | 1 | 0.01% |
| EDC0733 | 1 | 0.01% |
| EDC3641 | 1 | 0.01% |
| EDC3005 | 1 | 0.01% |
| EDC2993 | 1 | 0.01% |
| EDC0437 | 1 | 0.01% |
| EDC2106 | 1 | 0.01% |
| EDC0557 | 1 | 0.01% |
| EDC5155 | 1 | 0.01% |
| EDC4041 | 1 | 0.01% |
| EDC5227 | 1 | 0.01% |
| EDC1018 | 1 | 0.01% |
| EDC4960 | 1 | 0.01% |
| EDC2665 | 1 | 0.01% |
| EDC4607 | 1 | 0.01% |
| EDC4204 | 1 | 0.01% |
| EDC4068 | 1 | 0.01% |
| EDC3486 | 1 | 0.01% |
| EDC5137 | 1 | 0.01% |
| EDC4891 | 1 | 0.01% |
| EDC4334 | 1 | 0.01% |
| EDC1848 | 1 | 0.01% |
| EDC0418 | 1 | 0.01% |
| EDC4340 | 1 | 0.01% |
| EDC5077 | 1 | 0.01% |
| EDC2603 | 1 | 0.01% |
| EDC4809 | 1 | 0.01% |
| EDC3281 | 1 | 0.01% |
| EDC4745 | 1 | 0.01% |
| EDC2093 | 1 | 0.01% |
| EDC4489 | 1 | 0.01% |
| EDC3714 | 1 | 0.01% |
| EDC3893 | 1 | 0.01% |
| EDC2110 | 1 | 0.01% |
| EDC3736 | 1 | 0.01% |
| EDC1963 | 1 | 0.01% |
| EDC0592 | 1 | 0.01% |
| EDC0517 | 1 | 0.01% |
| EDC4126 | 1 | 0.01% |
| EDC4299 | 1 | 0.01% |
| EDC2276 | 1 | 0.01% |
| EDC0979 | 1 | 0.01% |
| EDC3912 | 1 | 0.01% |
| EDC4473 | 1 | 0.01% |
| EDC5008 | 1 | 0.01% |
| EDC2487 | 1 | 0.01% |
| EDC3078 | 1 | 0.01% |
| EDC2883 | 1 | 0.01% |
| EDC5305 | 1 | 0.01% |
| EDC1535 | 1 | 0.01% |
| EDC2555 | 1 | 0.01% |
| EDC3273 | 1 | 0.01% |
| EDC4972 | 1 | 0.01% |
| EDC1465 | 1 | 0.01% |
| EDC1928 | 1 | 0.01% |
| EDC0218 | 1 | 0.01% |
| EDC0624 | 1 | 0.01% |
| EDC0400 | 1 | 0.01% |
| EDC0594 | 1 | 0.01% |
| EDC3454 | 1 | 0.01% |
| EDC3706 | 1 | 0.01% |
| EDC5314 | 1 | 0.01% |
| EDC4743 | 1 | 0.01% |
| EDC1511 | 1 | 0.01% |
| EDC3152 | 1 | 0.01% |
| EDC2242 | 1 | 0.01% |
| EDC1689 | 1 | 0.01% |
| EDC5340 | 1 | 0.01% |
| EDC4846 | 1 | 0.01% |
| EDC4814 | 1 | 0.01% |
| EDC1448 | 1 | 0.01% |
| EDC0685 | 1 | 0.01% |
| EDC1418 | 1 | 0.01% |
| EDC2357 | 1 | 0.01% |
| EDC2558 | 1 | 0.01% |
| EDC0408 | 1 | 0.01% |
| EDC3114 | 1 | 0.01% |
| EDC3337 | 1 | 0.01% |
| EDC4729 | 1 | 0.01% |
| EDC0545 | 1 | 0.01% |
| EDC2958 | 1 | 0.01% |
| EDC3263 | 1 | 0.01% |
| EDC4864 | 1 | 0.01% |
| EDC4813 | 1 | 0.01% |
| EDC2582 | 1 | 0.01% |
| EDC3446 | 1 | 0.01% |
| EDC4725 | 1 | 0.01% |
| EDC3923 | 1 | 0.01% |
| EDC2531 | 1 | 0.01% |
| EDC4812 | 1 | 0.01% |
| EDC0841 | 1 | 0.01% |
| EDC1920 | 1 | 0.01% |
| EDC5301 | 1 | 0.01% |
| EDC4246 | 1 | 0.01% |
| EDC1168 | 1 | 0.01% |
| EDC2533 | 1 | 0.01% |
| EDC4356 | 1 | 0.01% |
| EDC3701 | 1 | 0.01% |
| EDC4336 | 1 | 0.01% |
| EDC2338 | 1 | 0.01% |
| EDC4410 | 1 | 0.01% |
| EDC0238 | 1 | 0.01% |
| EDC1542 | 1 | 0.01% |
| EDC0053 | 1 | 0.01% |
| EDC1102 | 1 | 0.01% |
| EDC1639 | 1 | 0.01% |
| EDC2525 | 1 | 0.01% |
| EDC1508 | 1 | 0.01% |
| EDC2899 | 1 | 0.01% |
| EDC3025 | 1 | 0.01% |
| EDC0393 | 1 | 0.01% |
| EDC3002 | 1 | 0.01% |
| EDC0039 | 1 | 0.01% |
| EDC0983 | 1 | 0.01% |
| EDC0590 | 1 | 0.01% |
| EDC1049 | 1 | 0.01% |
| EDC2366 | 1 | 0.01% |
| EDC3862 | 1 | 0.01% |
| EDC2941 | 1 | 0.01% |
| EDC1876 | 1 | 0.01% |
| EDC4659 | 1 | 0.01% |
| EDC2138 | 1 | 0.01% |
| EDC4019 | 1 | 0.01% |
| EDC4303 | 1 | 0.01% |
| EDC2745 | 1 | 0.01% |
| EDC4760 | 1 | 0.01% |
| EDC4780 | 1 | 0.01% |
| EDC1855 | 1 | 0.01% |
| EDC0359 | 1 | 0.01% |
| EDC0190 | 1 | 0.01% |
| EDC3965 | 1 | 0.01% |
| EDC5272 | 1 | 0.01% |
| EDC3986 | 1 | 0.01% |
| EDC1994 | 1 | 0.01% |
| EDC2791 | 1 | 0.01% |
| EDC4540 | 1 | 0.01% |
| EDC3603 | 1 | 0.01% |
| EDC3913 | 1 | 0.01% |
| EDC0356 | 1 | 0.01% |
| EDC0222 | 1 | 0.01% |
| EDC3942 | 1 | 0.01% |
| EDC0067 | 1 | 0.01% |
| EDC1123 | 1 | 0.01% |
| EDC1308 | 1 | 0.01% |
| EDC2622 | 1 | 0.01% |
| EDC2202 | 1 | 0.01% |
| EDC4603 | 1 | 0.01% |
| EDC3022 | 1 | 0.01% |
| EDC0964 | 1 | 0.01% |
| EDC1950 | 1 | 0.01% |
| EDC2670 | 1 | 0.01% |
| EDC1375 | 1 | 0.01% |
| EDC5253 | 1 | 0.01% |
| EDC4510 | 1 | 0.01% |
| EDC2117 | 1 | 0.01% |
| EDC1275 | 1 | 0.01% |
| EDC1518 | 1 | 0.01% |
| EDC4672 | 1 | 0.01% |
| EDC3129 | 1 | 0.01% |
| EDC0223 | 1 | 0.01% |
| EDC3238 | 1 | 0.01% |
| EDC0399 | 1 | 0.01% |
| EDC5038 | 1 | 0.01% |
| EDC3931 | 1 | 0.01% |
| EDC1624 | 1 | 0.01% |
| EDC0556 | 1 | 0.01% |
| EDC2324 | 1 | 0.01% |
| EDC3009 | 1 | 0.01% |
| EDC3729 | 1 | 0.01% |
| EDC0492 | 1 | 0.01% |
| EDC4750 | 1 | 0.01% |
| EDC1663 | 1 | 0.01% |
| EDC1485 | 1 | 0.01% |
| EDC4358 | 1 | 0.01% |
| EDC1698 | 1 | 0.01% |
| EDC2700 | 1 | 0.01% |
| EDC0738 | 1 | 0.01% |
| EDC3407 | 1 | 0.01% |
| EDC4036 | 1 | 0.01% |
| EDC0469 | 1 | 0.01% |
| EDC0512 | 1 | 0.01% |
| EDC1773 | 1 | 0.01% |
| EDC0914 | 1 | 0.01% |
| EDC2462 | 1 | 0.01% |
| EDC3362 | 1 | 0.01% |
| EDC5280 | 1 | 0.01% |
| EDC2808 | 1 | 0.01% |
| EDC0282 | 1 | 0.01% |
| EDC5025 | 1 | 0.01% |
| EDC1394 | 1 | 0.01% |
| EDC5135 | 1 | 0.01% |
| EDC3739 | 1 | 0.01% |
| EDC0226 | 1 | 0.01% |
| EDC3240 | 1 | 0.01% |
| EDC4238 | 1 | 0.01% |
| EDC3900 | 1 | 0.01% |
| EDC1245 | 1 | 0.01% |
| EDC3458 | 1 | 0.01% |
| EDC3186 | 1 | 0.01% |
| EDC0042 | 1 | 0.01% |
| EDC4313 | 1 | 0.01% |
| EDC2187 | 1 | 0.01% |
| EDC5046 | 1 | 0.01% |
| EDC0577 | 1 | 0.01% |
| EDC1580 | 1 | 0.01% |
| EDC3796 | 1 | 0.01% |
| EDC2097 | 1 | 0.01% |
| EDC0466 | 1 | 0.01% |
| EDC1613 | 1 | 0.01% |
| EDC1246 | 1 | 0.01% |
| EDC2690 | 1 | 0.01% |
| EDC4532 | 1 | 0.01% |
| EDC3940 | 1 | 0.01% |
| EDC4773 | 1 | 0.01% |
| EDC1717 | 1 | 0.01% |
| EDC3463 | 1 | 0.01% |
| EDC3718 | 1 | 0.01% |
| EDC2994 | 1 | 0.01% |
| EDC4803 | 1 | 0.01% |
| EDC0948 | 1 | 0.01% |
| EDC2131 | 1 | 0.01% |
| EDC4665 | 1 | 0.01% |
| EDC3294 | 1 | 0.01% |
| EDC1559 | 1 | 0.01% |
| EDC2182 | 1 | 0.01% |
| EDC1858 | 1 | 0.01% |
| EDC2492 | 1 | 0.01% |
| EDC1749 | 1 | 0.01% |
| EDC5073 | 1 | 0.01% |
| EDC1188 | 1 | 0.01% |
| EDC3512 | 1 | 0.01% |
| EDC2168 | 1 | 0.01% |
| EDC2950 | 1 | 0.01% |
| EDC0426 | 1 | 0.01% |
| EDC4806 | 1 | 0.01% |
| EDC3139 | 1 | 0.01% |
| EDC4586 | 1 | 0.01% |
| EDC4254 | 1 | 0.01% |
| EDC4075 | 1 | 0.01% |
| EDC3963 | 1 | 0.01% |
| EDC1149 | 1 | 0.01% |
| EDC1236 | 1 | 0.01% |
| EDC5244 | 1 | 0.01% |
| EDC2413 | 1 | 0.01% |
| EDC2244 | 1 | 0.01% |
| EDC2091 | 1 | 0.01% |
| EDC3915 | 1 | 0.01% |
| EDC0715 | 1 | 0.01% |
| EDC1184 | 1 | 0.01% |
| EDC2795 | 1 | 0.01% |
| EDC4679 | 1 | 0.01% |
| EDC5170 | 1 | 0.01% |
| EDC2748 | 1 | 0.01% |
| EDC3276 | 1 | 0.01% |
| EDC0785 | 1 | 0.01% |
| EDC0595 | 1 | 0.01% |
| EDC2693 | 1 | 0.01% |
| EDC1355 | 1 | 0.01% |
| EDC2299 | 1 | 0.01% |
| EDC2680 | 1 | 0.01% |
| EDC4520 | 1 | 0.01% |
| EDC4111 | 1 | 0.01% |
| EDC4613 | 1 | 0.01% |
| EDC5119 | 1 | 0.01% |
| EDC2147 | 1 | 0.01% |
| EDC4470 | 1 | 0.01% |
| EDC0805 | 1 | 0.01% |
| EDC3653 | 1 | 0.01% |
| EDC5220 | 1 | 0.01% |
| EDC0323 | 1 | 0.01% |
| EDC3634 | 1 | 0.01% |
| EDC4239 | 1 | 0.01% |
| EDC0996 | 1 | 0.01% |
| EDC0293 | 1 | 0.01% |
| EDC0716 | 1 | 0.01% |
| EDC4298 | 1 | 0.01% |
| EDC1941 | 1 | 0.01% |
| EDC3394 | 1 | 0.01% |
| EDC3222 | 1 | 0.01% |
| EDC0756 | 1 | 0.01% |
| EDC2394 | 1 | 0.01% |
| EDC4257 | 1 | 0.01% |
| EDC0471 | 1 | 0.01% |
| EDC2156 | 1 | 0.01% |
| EDC1725 | 1 | 0.01% |
| EDC4741 | 1 | 0.01% |
| EDC1504 | 1 | 0.01% |
| EDC1708 | 1 | 0.01% |
| EDC0189 | 1 | 0.01% |
| EDC5234 | 1 | 0.01% |
| EDC1643 | 1 | 0.01% |
| EDC2111 | 1 | 0.01% |
| EDC0593 | 1 | 0.01% |
| EDC5250 | 1 | 0.01% |
| EDC0346 | 1 | 0.01% |
| EDC3390 | 1 | 0.01% |
| EDC5090 | 1 | 0.01% |
| EDC3169 | 1 | 0.01% |
| EDC4585 | 1 | 0.01% |
| EDC2857 | 1 | 0.01% |
| EDC4619 | 1 | 0.01% |
| EDC1891 | 1 | 0.01% |
| EDC4403 | 1 | 0.01% |
| EDC2679 | 1 | 0.01% |
| EDC1005 | 1 | 0.01% |
| EDC3564 | 1 | 0.01% |
| EDC1856 | 1 | 0.01% |
| EDC3505 | 1 | 0.01% |
| EDC5022 | 1 | 0.01% |
| EDC4969 | 1 | 0.01% |
| EDC3057 | 1 | 0.01% |
| EDC2101 | 1 | 0.01% |
| EDC0657 | 1 | 0.01% |
| EDC1341 | 1 | 0.01% |
| EDC2484 | 1 | 0.01% |
| EDC3877 | 1 | 0.01% |
| EDC2309 | 1 | 0.01% |
| EDC4755 | 1 | 0.01% |
| EDC5256 | 1 | 0.01% |
| EDC0248 | 1 | 0.01% |
| EDC1263 | 1 | 0.01% |
| EDC2162 | 1 | 0.01% |
| EDC0432 | 1 | 0.01% |
| EDC3143 | 1 | 0.01% |
| EDC1126 | 1 | 0.01% |
| EDC0686 | 1 | 0.01% |
| EDC0308 | 1 | 0.01% |
| EDC4522 | 1 | 0.01% |
| EDC0806 | 1 | 0.01% |
| EDC2671 | 1 | 0.01% |
| EDC3324 | 1 | 0.01% |
| EDC4282 | 1 | 0.01% |
| EDC1821 | 1 | 0.01% |
| EDC4454 | 1 | 0.01% |
| EDC2261 | 1 | 0.01% |
| EDC2509 | 1 | 0.01% |
| EDC0490 | 1 | 0.01% |
| EDC1603 | 1 | 0.01% |
| EDC4484 | 1 | 0.01% |
| EDC1610 | 1 | 0.01% |
| EDC2102 | 1 | 0.01% |
| EDC3036 | 1 | 0.01% |
| EDC0533 | 1 | 0.01% |
| EDC3223 | 1 | 0.01% |
| EDC0374 | 1 | 0.01% |
| EDC3725 | 1 | 0.01% |
| EDC4307 | 1 | 0.01% |
| EDC4494 | 1 | 0.01% |
| EDC2342 | 1 | 0.01% |
| EDC0911 | 1 | 0.01% |
| EDC2667 | 1 | 0.01% |
| EDC1806 | 1 | 0.01% |
| EDC1109 | 1 | 0.01% |
| EDC0336 | 1 | 0.01% |
| EDC2546 | 1 | 0.01% |
| EDC2220 | 1 | 0.01% |
| EDC3178 | 1 | 0.01% |
| EDC0513 | 1 | 0.01% |
| EDC0531 | 1 | 0.01% |
| EDC2298 | 1 | 0.01% |
| EDC2659 | 1 | 0.01% |
| EDC0052 | 1 | 0.01% |
| EDC4869 | 1 | 0.01% |
| EDC3487 | 1 | 0.01% |
| EDC2451 | 1 | 0.01% |
| EDC4118 | 1 | 0.01% |
| EDC4270 | 1 | 0.01% |
| EDC4310 | 1 | 0.01% |
| EDC1419 | 1 | 0.01% |
| EDC1845 | 1 | 0.01% |
| EDC4197 | 1 | 0.01% |
| EDC4032 | 1 | 0.01% |
| EDC1760 | 1 | 0.01% |
| EDC2763 | 1 | 0.01% |
| EDC3084 | 1 | 0.01% |
| EDC0926 | 1 | 0.01% |
| EDC2056 | 1 | 0.01% |
| EDC4105 | 1 | 0.01% |
| EDC1163 | 1 | 0.01% |
| EDC0790 | 1 | 0.01% |
| EDC3854 | 1 | 0.01% |
| EDC0392 | 1 | 0.01% |
| EDC2971 | 1 | 0.01% |
| EDC0737 | 1 | 0.01% |
| EDC3617 | 1 | 0.01% |
| EDC3612 | 1 | 0.01% |
| EDC2210 | 1 | 0.01% |
| EDC2436 | 1 | 0.01% |
| EDC4838 | 1 | 0.01% |
| EDC2858 | 1 | 0.01% |
| EDC5084 | 1 | 0.01% |
| EDC0233 | 1 | 0.01% |
| EDC2079 | 1 | 0.01% |
| EDC4119 | 1 | 0.01% |
| EDC3453 | 1 | 0.01% |
| EDC2560 | 1 | 0.01% |
| EDC3929 | 1 | 0.01% |
| EDC0644 | 1 | 0.01% |
| EDC4778 | 1 | 0.01% |
| EDC1272 | 1 | 0.01% |
| EDC4697 | 1 | 0.01% |
| EDC4463 | 1 | 0.01% |
| EDC5299 | 1 | 0.01% |
| EDC1634 | 1 | 0.01% |
| EDC4953 | 1 | 0.01% |
| EDC0874 | 1 | 0.01% |
| EDC1070 | 1 | 0.01% |
| EDC4404 | 1 | 0.01% |
| EDC3865 | 1 | 0.01% |
| EDC1330 | 1 | 0.01% |
| EDC0022 | 1 | 0.01% |
| EDC1234 | 1 | 0.01% |
| EDC5115 | 1 | 0.01% |
| EDC3757 | 1 | 0.01% |
| EDC4833 | 1 | 0.01% |
| EDC5200 | 1 | 0.01% |
| EDC1737 | 1 | 0.01% |
| EDC0483 | 1 | 0.01% |
| EDC3405 | 1 | 0.01% |
| EDC3366 | 1 | 0.01% |
| EDC1812 | 1 | 0.01% |
| EDC3661 | 1 | 0.01% |
| EDC2214 | 1 | 0.01% |
| EDC1075 | 1 | 0.01% |
| EDC4575 | 1 | 0.01% |
| EDC5251 | 1 | 0.01% |
| EDC1793 | 1 | 0.01% |
| EDC3513 | 1 | 0.01% |
| EDC1780 | 1 | 0.01% |
| EDC3352 | 1 | 0.01% |
| EDC1861 | 1 | 0.01% |
| EDC2359 | 1 | 0.01% |
| EDC3157 | 1 | 0.01% |
| EDC3369 | 1 | 0.01% |
| EDC2384 | 1 | 0.01% |
| EDC1043 | 1 | 0.01% |
| EDC4261 | 1 | 0.01% |
| EDC0215 | 1 | 0.01% |
| EDC2274 | 1 | 0.01% |
| EDC1638 | 1 | 0.01% |
| EDC4259 | 1 | 0.01% |
| EDC2271 | 1 | 0.01% |
| EDC2586 | 1 | 0.01% |
| EDC1670 | 1 | 0.01% |
| EDC4923 | 1 | 0.01% |
| EDC0245 | 1 | 0.01% |
| EDC0818 | 1 | 0.01% |
| EDC1614 | 1 | 0.01% |
| EDC4160 | 1 | 0.01% |
| EDC3126 | 1 | 0.01% |
| EDC0529 | 1 | 0.01% |
| EDC1285 | 1 | 0.01% |
| EDC0344 | 1 | 0.01% |
| EDC4782 | 1 | 0.01% |
| EDC4742 | 1 | 0.01% |
| EDC4411 | 1 | 0.01% |
| EDC0547 | 1 | 0.01% |
| EDC1731 | 1 | 0.01% |
| EDC3122 | 1 | 0.01% |
| EDC4491 | 1 | 0.01% |
| EDC5140 | 1 | 0.01% |
| EDC5278 | 1 | 0.01% |
| EDC4219 | 1 | 0.01% |
| EDC4945 | 1 | 0.01% |
| EDC0589 | 1 | 0.01% |
| EDC1652 | 1 | 0.01% |
| EDC4362 | 1 | 0.01% |
| EDC2651 | 1 | 0.01% |
| EDC1264 | 1 | 0.01% |
| EDC4278 | 1 | 0.01% |
| EDC4521 | 1 | 0.01% |
| EDC4983 | 1 | 0.01% |
| EDC0793 | 1 | 0.01% |
| EDC2039 | 1 | 0.01% |
| EDC0721 | 1 | 0.01% |
| EDC4385 | 1 | 0.01% |
| EDC3061 | 1 | 0.01% |
| EDC2777 | 1 | 0.01% |
| EDC1592 | 1 | 0.01% |
| EDC5326 | 1 | 0.01% |
| EDC2213 | 1 | 0.01% |
| EDC0789 | 1 | 0.01% |
| EDC3849 | 1 | 0.01% |
| EDC2166 | 1 | 0.01% |
| EDC5292 | 1 | 0.01% |
| EDC3761 | 1 | 0.01% |
| EDC0916 | 1 | 0.01% |
| EDC2508 | 1 | 0.01% |
| EDC1986 | 1 | 0.01% |
| EDC4580 | 1 | 0.01% |
| EDC0530 | 1 | 0.01% |
| EDC2880 | 1 | 0.01% |
| EDC3216 | 1 | 0.01% |
| EDC3258 | 1 | 0.01% |
| EDC4925 | 1 | 0.01% |
| EDC2739 | 1 | 0.01% |
| EDC2408 | 1 | 0.01% |
| EDC5167 | 1 | 0.01% |
| EDC2561 | 1 | 0.01% |
| EDC5069 | 1 | 0.01% |
| EDC2211 | 1 | 0.01% |
| EDC2179 | 1 | 0.01% |
| EDC1076 | 1 | 0.01% |
| EDC1893 | 1 | 0.01% |
| EDC3731 | 1 | 0.01% |
| EDC4480 | 1 | 0.01% |
| EDC4875 | 1 | 0.01% |
| EDC5294 | 1 | 0.01% |
| EDC0283 | 1 | 0.01% |
| EDC2523 | 1 | 0.01% |
| EDC2768 | 1 | 0.01% |
| EDC1981 | 1 | 0.01% |
| EDC3124 | 1 | 0.01% |
| EDC4653 | 1 | 0.01% |
| EDC2417 | 1 | 0.01% |
| EDC4538 | 1 | 0.01% |
| EDC1800 | 1 | 0.01% |
| EDC3160 | 1 | 0.01% |
| EDC0987 | 1 | 0.01% |
| EDC2432 | 1 | 0.01% |
| EDC1839 | 1 | 0.01% |
| EDC1693 | 1 | 0.01% |
| EDC3428 | 1 | 0.01% |
| EDC3156 | 1 | 0.01% |
| EDC1546 | 1 | 0.01% |
| EDC5138 | 1 | 0.01% |
| EDC3570 | 1 | 0.01% |
| EDC4807 | 1 | 0.01% |
| EDC3618 | 1 | 0.01% |
| EDC0348 | 1 | 0.01% |
| EDC0776 | 1 | 0.01% |
| EDC5123 | 1 | 0.01% |
| EDC3890 | 1 | 0.01% |
| EDC3320 | 1 | 0.01% |
| EDC3330 | 1 | 0.01% |
| EDC0373 | 1 | 0.01% |
| EDC1042 | 1 | 0.01% |
| EDC3361 | 1 | 0.01% |
| EDC2350 | 1 | 0.01% |
| EDC4386 | 1 | 0.01% |
| EDC4615 | 1 | 0.01% |
| EDC2842 | 1 | 0.01% |
| EDC0581 | 1 | 0.01% |
| EDC3433 | 1 | 0.01% |
| EDC4380 | 1 | 0.01% |
| EDC0937 | 1 | 0.01% |
| EDC0410 | 1 | 0.01% |
| EDC2463 | 1 | 0.01% |
| EDC4396 | 1 | 0.01% |
| EDC2454 | 1 | 0.01% |
| EDC3532 | 1 | 0.01% |
| EDC2023 | 1 | 0.01% |
| EDC0876 | 1 | 0.01% |
| EDC3062 | 1 | 0.01% |
| EDC2856 | 1 | 0.01% |
| EDC4965 | 1 | 0.01% |
| EDC2071 | 1 | 0.01% |
| EDC3853 | 1 | 0.01% |
| EDC3784 | 1 | 0.01% |
| EDC2252 | 1 | 0.01% |
| EDC2811 | 1 | 0.01% |
| EDC4146 | 1 | 0.01% |
| EDC3582 | 1 | 0.01% |
| EDC1816 | 1 | 0.01% |
| EDC2046 | 1 | 0.01% |
| EDC0385 | 1 | 0.01% |
| EDC4498 | 1 | 0.01% |
| EDC3957 | 1 | 0.01% |
| EDC1150 | 1 | 0.01% |
| EDC2277 | 1 | 0.01% |
| EDC3534 | 1 | 0.01% |
| EDC4429 | 1 | 0.01% |
| EDC3251 | 1 | 0.01% |
| EDC2158 | 1 | 0.01% |
| EDC1841 | 1 | 0.01% |
| EDC0128 | 1 | 0.01% |
| EDC3173 | 1 | 0.01% |
| EDC4861 | 1 | 0.01% |
| EDC0227 | 1 | 0.01% |
| EDC2827 | 1 | 0.01% |
| EDC2996 | 1 | 0.01% |
| EDC1015 | 1 | 0.01% |
| EDC1797 | 1 | 0.01% |
| EDC3779 | 1 | 0.01% |
| EDC2060 | 1 | 0.01% |
| EDC1791 | 1 | 0.01% |
| EDC0205 | 1 | 0.01% |
| EDC4984 | 1 | 0.01% |
| EDC2773 | 1 | 0.01% |
| EDC4589 | 1 | 0.01% |
| EDC4305 | 1 | 0.01% |
| EDC5127 | 1 | 0.01% |
| EDC3262 | 1 | 0.01% |
| EDC0372 | 1 | 0.01% |
| EDC3489 | 1 | 0.01% |
| EDC3716 | 1 | 0.01% |
| EDC4746 | 1 | 0.01% |
| EDC5297 | 1 | 0.01% |
| EDC2542 | 1 | 0.01% |
| EDC3462 | 1 | 0.01% |
| EDC3140 | 1 | 0.01% |
| EDC2649 | 1 | 0.01% |
| EDC3547 | 1 | 0.01% |
| EDC1754 | 1 | 0.01% |
| EDC4816 | 1 | 0.01% |
| EDC2164 | 1 | 0.01% |
| EDC1541 | 1 | 0.01% |
| EDC4172 | 1 | 0.01% |
| EDC3834 | 1 | 0.01% |
| EDC3766 | 1 | 0.01% |
| EDC1944 | 1 | 0.01% |
| EDC4931 | 1 | 0.01% |
| EDC3601 | 1 | 0.01% |
| EDC2612 | 1 | 0.01% |
| EDC1640 | 1 | 0.01% |
| EDC0213 | 1 | 0.01% |
| EDC0084 | 1 | 0.01% |
| EDC1134 | 1 | 0.01% |
| EDC3357 | 1 | 0.01% |
| EDC3095 | 1 | 0.01% |
| EDC3237 | 1 | 0.01% |
| EDC2373 | 1 | 0.01% |
| EDC3543 | 1 | 0.01% |
| EDC0024 | 1 | 0.01% |
| EDC0975 | 1 | 0.01% |
| EDC3937 | 1 | 0.01% |
| EDC5318 | 1 | 0.01% |
| EDC1344 | 1 | 0.01% |
| EDC0767 | 1 | 0.01% |
| EDC4229 | 1 | 0.01% |
| EDC1060 | 1 | 0.01% |
| EDC3292 | 1 | 0.01% |
| EDC4859 | 1 | 0.01% |
| EDC1563 | 1 | 0.01% |
| EDC5336 | 1 | 0.01% |
| EDC1108 | 1 | 0.01% |
| EDC4001 | 1 | 0.01% |
| EDC4431 | 1 | 0.01% |
| EDC4029 | 1 | 0.01% |
| EDC3959 | 1 | 0.01% |
| EDC2030 | 1 | 0.01% |
| EDC0417 | 1 | 0.01% |
| EDC3586 | 1 | 0.01% |
| EDC0929 | 1 | 0.01% |
| EDC4902 | 1 | 0.01% |
| EDC0867 | 1 | 0.01% |
| EDC0555 | 1 | 0.01% |
| EDC5139 | 1 | 0.01% |
| EDC3312 | 1 | 0.01% |
| EDC0506 | 1 | 0.01% |
| EDC1587 | 1 | 0.01% |
| EDC4135 | 1 | 0.01% |
| EDC0389 | 1 | 0.01% |
| EDC4150 | 1 | 0.01% |
| EDC5315 | 1 | 0.01% |
| EDC0844 | 1 | 0.01% |
| EDC1736 | 1 | 0.01% |
| EDC2034 | 1 | 0.01% |
| EDC4459 | 1 | 0.01% |
| EDC3927 | 1 | 0.01% |
| EDC2011 | 1 | 0.01% |
| EDC0949 | 1 | 0.01% |
| EDC2072 | 1 | 0.01% |
| EDC0330 | 1 | 0.01% |
| EDC1310 | 1 | 0.01% |
| EDC2595 | 1 | 0.01% |
| EDC2192 | 1 | 0.01% |
| EDC1248 | 1 | 0.01% |
| EDC0773 | 1 | 0.01% |
| EDC4492 | 1 | 0.01% |
| EDC4134 | 1 | 0.01% |
| EDC2556 | 1 | 0.01% |
| EDC2283 | 1 | 0.01% |
| EDC4363 | 1 | 0.01% |
| EDC1903 | 1 | 0.01% |
| EDC4434 | 1 | 0.01% |
| EDC1185 | 1 | 0.01% |
| EDC2902 | 1 | 0.01% |
| EDC3835 | 1 | 0.01% |
| EDC1533 | 1 | 0.01% |
| EDC5049 | 1 | 0.01% |
| EDC0639 | 1 | 0.01% |
| EDC4360 | 1 | 0.01% |
| EDC5125 | 1 | 0.01% |
| EDC2624 | 1 | 0.01% |
| EDC4541 | 1 | 0.01% |
| EDC1249 | 1 | 0.01% |
| EDC4240 | 1 | 0.01% |
| EDC3528 | 1 | 0.01% |
| EDC1279 | 1 | 0.01% |
| EDC2864 | 1 | 0.01% |
| EDC1252 | 1 | 0.01% |
| EDC4413 | 1 | 0.01% |
| EDC4127 | 1 | 0.01% |
| EDC1063 | 1 | 0.01% |
| EDC3436 | 1 | 0.01% |
| EDC0613 | 1 | 0.01% |
| EDC0064 | 1 | 0.01% |
| EDC4301 | 1 | 0.01% |
| EDC0740 | 1 | 0.01% |
| EDC3891 | 1 | 0.01% |
| EDC0923 | 1 | 0.01% |
| EDC3414 | 1 | 0.01% |
| EDC0494 | 1 | 0.01% |
| EDC1795 | 1 | 0.01% |
| EDC1189 | 1 | 0.01% |
| EDC1525 | 1 | 0.01% |
| EDC2642 | 1 | 0.01% |
| EDC0775 | 1 | 0.01% |
| EDC4324 | 1 | 0.01% |
| EDC2226 | 1 | 0.01% |
| EDC2701 | 1 | 0.01% |
| EDC4944 | 1 | 0.01% |
| EDC0130 | 1 | 0.01% |
| EDC1940 | 1 | 0.01% |
| EDC4811 | 1 | 0.01% |
| EDC4801 | 1 | 0.01% |
| EDC1282 | 1 | 0.01% |
| EDC0079 | 1 | 0.01% |
| EDC3536 | 1 | 0.01% |
| EDC2675 | 1 | 0.01% |
| EDC4831 | 1 | 0.01% |
| EDC1395 | 1 | 0.01% |
| EDC0482 | 1 | 0.01% |
| EDC2500 | 1 | 0.01% |
| EDC4985 | 1 | 0.01% |
| EDC5177 | 1 | 0.01% |
| EDC3945 | 1 | 0.01% |
| EDC1441 | 1 | 0.01% |
| EDC1924 | 1 | 0.01% |
| EDC4873 | 1 | 0.01% |
| EDC1804 | 1 | 0.01% |
| EDC4747 | 1 | 0.01% |
| EDC2243 | 1 | 0.01% |
| EDC2358 | 1 | 0.01% |
| EDC2476 | 1 | 0.01% |
| EDC0113 | 1 | 0.01% |
| EDC2705 | 1 | 0.01% |
| EDC3372 | 1 | 0.01% |
| EDC0620 | 1 | 0.01% |
| EDC1844 | 1 | 0.01% |
| EDC3325 | 1 | 0.01% |
| EDC2167 | 1 | 0.01% |
| EDC3250 | 1 | 0.01% |
| EDC1561 | 1 | 0.01% |
| EDC1359 | 1 | 0.01% |
| EDC4481 | 1 | 0.01% |
| EDC1466 | 1 | 0.01% |
| EDC0034 | 1 | 0.01% |
| EDC3029 | 1 | 0.01% |
| EDC4083 | 1 | 0.01% |
| EDC0900 | 1 | 0.01% |
| EDC0191 | 1 | 0.01% |
| EDC2222 | 1 | 0.01% |
| EDC3885 | 1 | 0.01% |
| EDC3351 | 1 | 0.01% |
| EDC1942 | 1 | 0.01% |
| EDC0546 | 1 | 0.01% |
| EDC3284 | 1 | 0.01% |
| EDC3115 | 1 | 0.01% |
| EDC0360 | 1 | 0.01% |
| EDC2787 | 1 | 0.01% |
| EDC4681 | 1 | 0.01% |
| EDC4901 | 1 | 0.01% |
| EDC4821 | 1 | 0.01% |
| EDC3241 | 1 | 0.01% |
| EDC2783 | 1 | 0.01% |
| EDC4465 | 1 | 0.01% |
| EDC2695 | 1 | 0.01% |
| EDC4606 | 1 | 0.01% |
| EDC0278 | 1 | 0.01% |
| EDC2885 | 1 | 0.01% |
| EDC1554 | 1 | 0.01% |
| EDC4329 | 1 | 0.01% |
| EDC4674 | 1 | 0.01% |
| EDC0266 | 1 | 0.01% |
| EDC4241 | 1 | 0.01% |
| EDC0861 | 1 | 0.01% |
| EDC1526 | 1 | 0.01% |
| EDC4401 | 1 | 0.01% |
| EDC4499 | 1 | 0.01% |
| EDC3201 | 1 | 0.01% |
| EDC1271 | 1 | 0.01% |
| EDC5169 | 1 | 0.01% |
| EDC0924 | 1 | 0.01% |
| EDC4834 | 1 | 0.01% |
| EDC1969 | 1 | 0.01% |
| EDC2992 | 1 | 0.01% |
| EDC0211 | 1 | 0.01% |
| EDC2949 | 1 | 0.01% |
| EDC3179 | 1 | 0.01% |
| EDC0376 | 1 | 0.01% |
| EDC2846 | 1 | 0.01% |
| EDC3899 | 1 | 0.01% |
| EDC2278 | 1 | 0.01% |
| EDC3134 | 1 | 0.01% |
| EDC5108 | 1 | 0.01% |
| EDC1113 | 1 | 0.01% |
| EDC3082 | 1 | 0.01% |
| EDC4388 | 1 | 0.01% |
| EDC4791 | 1 | 0.01% |
| EDC1695 | 1 | 0.01% |
| EDC2356 | 1 | 0.01% |
| EDC4266 | 1 | 0.01% |
| EDC2157 | 1 | 0.01% |
| EDC1208 | 1 | 0.01% |
| EDC0099 | 1 | 0.01% |
| EDC1759 | 1 | 0.01% |
| EDC2566 | 1 | 0.01% |
| EDC3291 | 1 | 0.01% |
| EDC0056 | 1 | 0.01% |
| EDC3943 | 1 | 0.01% |
| EDC4551 | 1 | 0.01% |
| EDC0906 | 1 | 0.01% |
| EDC0325 | 1 | 0.01% |
| EDC4907 | 1 | 0.01% |
| EDC1050 | 1 | 0.01% |
| EDC4675 | 1 | 0.01% |
| EDC0340 | 1 | 0.01% |
| EDC4577 | 1 | 0.01% |
| EDC1294 | 1 | 0.01% |
| EDC3889 | 1 | 0.01% |
| EDC2571 | 1 | 0.01% |
| EDC3410 | 1 | 0.01% |
| EDC2009 | 1 | 0.01% |
| EDC4579 | 1 | 0.01% |
| EDC4739 | 1 | 0.01% |
| EDC1894 | 1 | 0.01% |
| EDC5087 | 1 | 0.01% |
| EDC0112 | 1 | 0.01% |
| EDC2464 | 1 | 0.01% |
| EDC1744 | 1 | 0.01% |
| EDC0918 | 1 | 0.01% |
| EDC4393 | 1 | 0.01% |
| EDC3947 | 1 | 0.01% |
| EDC3670 | 1 | 0.01% |
| EDC3244 | 1 | 0.01% |
| EDC0337 | 1 | 0.01% |
| EDC4013 | 1 | 0.01% |
| EDC4860 | 1 | 0.01% |
| EDC1257 | 1 | 0.01% |
| EDC0120 | 1 | 0.01% |
| EDC2794 | 1 | 0.01% |
| EDC0640 | 1 | 0.01% |
| EDC0202 | 1 | 0.01% |
| EDC0774 | 1 | 0.01% |
| EDC4402 | 1 | 0.01% |
| EDC0617 | 1 | 0.01% |
| EDC3987 | 1 | 0.01% |
| EDC1912 | 1 | 0.01% |
| EDC1852 | 1 | 0.01% |
| EDC0036 | 1 | 0.01% |
| EDC5180 | 1 | 0.01% |
| EDC1581 | 1 | 0.01% |
| EDC4115 | 1 | 0.01% |
| EDC2227 | 1 | 0.01% |
| EDC4351 | 1 | 0.01% |
| EDC1269 | 1 | 0.01% |
| EDC3028 | 1 | 0.01% |
| EDC0050 | 1 | 0.01% |
| EDC4693 | 1 | 0.01% |
| EDC2923 | 1 | 0.01% |
| EDC2504 | 1 | 0.01% |
| EDC0229 | 1 | 0.01% |
| EDC4144 | 1 | 0.01% |
| EDC2063 | 1 | 0.01% |
| EDC4312 | 1 | 0.01% |
| EDC0333 | 1 | 0.01% |
| EDC3170 | 1 | 0.01% |
| EDC0665 | 1 | 0.01% |
| EDC1815 | 1 | 0.01% |
| EDC0294 | 1 | 0.01% |
| EDC0480 | 1 | 0.01% |
| EDC0755 | 1 | 0.01% |
| EDC0664 | 1 | 0.01% |
| EDC0528 | 1 | 0.01% |
| EDC5158 | 1 | 0.01% |
| EDC2297 | 1 | 0.01% |
| EDC0815 | 1 | 0.01% |
| EDC5161 | 1 | 0.01% |
| EDC4189 | 1 | 0.01% |
| EDC1392 | 1 | 0.01% |
| EDC1933 | 1 | 0.01% |
| EDC4625 | 1 | 0.01% |
| EDC5183 | 1 | 0.01% |
| EDC4286 | 1 | 0.01% |
| EDC5339 | 1 | 0.01% |
| EDC4085 | 1 | 0.01% |
| EDC1415 | 1 | 0.01% |
| EDC2851 | 1 | 0.01% |
| EDC3151 | 1 | 0.01% |
| EDC2465 | 1 | 0.01% |
| EDC3936 | 1 | 0.01% |
| EDC2650 | 1 | 0.01% |
| EDC5148 | 1 | 0.01% |
| EDC0375 | 1 | 0.01% |
| EDC3550 | 1 | 0.01% |
| EDC2010 | 1 | 0.01% |
| EDC2637 | 1 | 0.01% |
| EDC3261 | 1 | 0.01% |
| EDC2908 | 1 | 0.01% |
| EDC3771 | 1 | 0.01% |
| EDC3809 | 1 | 0.01% |
| EDC2459 | 1 | 0.01% |
| EDC4933 | 1 | 0.01% |
| EDC1031 | 1 | 0.01% |
| EDC1107 | 1 | 0.01% |
| EDC3182 | 1 | 0.01% |
| EDC3413 | 1 | 0.01% |
| EDC2191 | 1 | 0.01% |
| EDC4614 | 1 | 0.01% |
| EDC4295 | 1 | 0.01% |
| EDC1958 | 1 | 0.01% |
| EDC5277 | 1 | 0.01% |
| EDC3421 | 1 | 0.01% |
| EDC0448 | 1 | 0.01% |
| EDC0772 | 1 | 0.01% |
| EDC4370 | 1 | 0.01% |
| EDC3648 | 1 | 0.01% |
| EDC4573 | 1 | 0.01% |
| EDC2577 | 1 | 0.01% |
| EDC0781 | 1 | 0.01% |
| EDC2264 | 1 | 0.01% |
| EDC2469 | 1 | 0.01% |
| EDC1228 | 1 | 0.01% |
| EDC2337 | 1 | 0.01% |
| EDC1174 | 1 | 0.01% |
| EDC1938 | 1 | 0.01% |
| EDC2040 | 1 | 0.01% |
| EDC1409 | 1 | 0.01% |
| EDC2349 | 1 | 0.01% |
| EDC1082 | 1 | 0.01% |
| EDC2720 | 1 | 0.01% |
| EDC4842 | 1 | 0.01% |
| EDC1124 | 1 | 0.01% |
| EDC2799 | 1 | 0.01% |
| EDC0260 | 1 | 0.01% |
| EDC0991 | 1 | 0.01% |
| EDC3770 | 1 | 0.01% |
| EDC1169 | 1 | 0.01% |
| EDC2209 | 1 | 0.01% |
| EDC4702 | 1 | 0.01% |
| EDC0599 | 1 | 0.01% |
| EDC0243 | 1 | 0.01% |
| EDC4128 | 1 | 0.01% |
| EDC3199 | 1 | 0.01% |
| EDC4423 | 1 | 0.01% |
| EDC3482 | 1 | 0.01% |
| EDC0936 | 1 | 0.01% |
| EDC2239 | 1 | 0.01% |
| EDC3595 | 1 | 0.01% |
| EDC2223 | 1 | 0.01% |
| EDC3374 | 1 | 0.01% |
| EDC4887 | 1 | 0.01% |
| EDC4302 | 1 | 0.01% |
| EDC2688 | 1 | 0.01% |
| EDC4045 | 1 | 0.01% |
| EDC2890 | 1 | 0.01% |
| EDC2526 | 1 | 0.01% |
| EDC4461 | 1 | 0.01% |
| EDC3981 | 1 | 0.01% |
| EDC4159 | 1 | 0.01% |
| EDC2319 | 1 | 0.01% |
| EDC0321 | 1 | 0.01% |
| EDC2881 | 1 | 0.01% |
| EDC5053 | 1 | 0.01% |
| EDC1930 | 1 | 0.01% |
| EDC3696 | 1 | 0.01% |
| EDC3992 | 1 | 0.01% |
| EDC4230 | 1 | 0.01% |
| EDC2750 | 1 | 0.01% |
| EDC2823 | 1 | 0.01% |
| EDC1932 | 1 | 0.01% |
| EDC3789 | 1 | 0.01% |
| EDC0618 | 1 | 0.01% |
| EDC3474 | 1 | 0.01% |
| EDC2694 | 1 | 0.01% |
| EDC3911 | 1 | 0.01% |
| EDC1877 | 1 | 0.01% |
| EDC3833 | 1 | 0.01% |
| EDC5230 | 1 | 0.01% |
| EDC2909 | 1 | 0.01% |
| EDC3051 | 1 | 0.01% |
| EDC4291 | 1 | 0.01% |
| EDC1596 | 1 | 0.01% |
| EDC0994 | 1 | 0.01% |
| EDC4503 | 1 | 0.01% |
| EDC3007 | 1 | 0.01% |
| EDC2610 | 1 | 0.01% |
| EDC5044 | 1 | 0.01% |
| EDC0915 | 1 | 0.01% |
| EDC1474 | 1 | 0.01% |
| EDC0295 | 1 | 0.01% |
| EDC2703 | 1 | 0.01% |
| EDC0959 | 1 | 0.01% |
| EDC0758 | 1 | 0.01% |
| EDC5281 | 1 | 0.01% |
| EDC0094 | 1 | 0.01% |
| EDC4874 | 1 | 0.01% |
| EDC3768 | 1 | 0.01% |
| EDC3252 | 1 | 0.01% |
| EDC2677 | 1 | 0.01% |
| EDC1931 | 1 | 0.01% |
| EDC3811 | 1 | 0.01% |
| EDC0944 | 1 | 0.01% |
| EDC1390 | 1 | 0.01% |
| EDC4447 | 1 | 0.01% |
| EDC2782 | 1 | 0.01% |
| EDC0780 | 1 | 0.01% |
| EDC4138 | 1 | 0.01% |
| EDC4926 | 1 | 0.01% |
| EDC4347 | 1 | 0.01% |
| EDC3692 | 1 | 0.01% |
| EDC2165 | 1 | 0.01% |
| EDC2977 | 1 | 0.01% |
| EDC3693 | 1 | 0.01% |
| EDC3562 | 1 | 0.01% |
| EDC2850 | 1 | 0.01% |
| EDC4417 | 1 | 0.01% |
| EDC1280 | 1 | 0.01% |
| EDC2019 | 1 | 0.01% |
| EDC3336 | 1 | 0.01% |
| EDC3755 | 1 | 0.01% |
| EDC0239 | 1 | 0.01% |
| EDC1694 | 1 | 0.01% |
| EDC2475 | 1 | 0.01% |
| EDC0860 | 1 | 0.01% |
| EDC4610 | 1 | 0.01% |
| EDC1035 | 1 | 0.01% |
| EDC0137 | 1 | 0.01% |
| EDC3018 | 1 | 0.01% |
| EDC1637 | 1 | 0.01% |
| EDC2435 | 1 | 0.01% |
| EDC3147 | 1 | 0.01% |
| EDC1562 | 1 | 0.01% |
| EDC0328 | 1 | 0.01% |
| EDC3933 | 1 | 0.01% |
| EDC5268 | 1 | 0.01% |
| EDC4416 | 1 | 0.01% |
| EDC1376 | 1 | 0.01% |
| EDC4683 | 1 | 0.01% |
| EDC4877 | 1 | 0.01% |
| EDC5023 | 1 | 0.01% |
| EDC5210 | 1 | 0.01% |
| EDC0643 | 1 | 0.01% |
| EDC4002 | 1 | 0.01% |
| EDC3590 | 1 | 0.01% |
| EDC0575 | 1 | 0.01% |
| EDC5275 | 1 | 0.01% |
| EDC1414 | 1 | 0.01% |
| EDC4415 | 1 | 0.01% |
| EDC1545 | 1 | 0.01% |
| EDC0872 | 1 | 0.01% |
| EDC3509 | 1 | 0.01% |
| EDC2987 | 1 | 0.01% |
| EDC0541 | 1 | 0.01% |
| EDC1750 | 1 | 0.01% |
| EDC3872 | 1 | 0.01% |
| EDC2834 | 1 | 0.01% |
| EDC0005 | 1 | 0.01% |
| EDC3622 | 1 | 0.01% |
| EDC4435 | 1 | 0.01% |
| EDC3542 | 1 | 0.01% |
| EDC3764 | 1 | 0.01% |
| EDC3003 | 1 | 0.01% |
| EDC1556 | 1 | 0.01% |
| EDC4766 | 1 | 0.01% |
| EDC5212 | 1 | 0.01% |
| EDC0459 | 1 | 0.01% |
| EDC3650 | 1 | 0.01% |
| EDC3876 | 1 | 0.01% |
| EDC3548 | 1 | 0.01% |
| EDC5219 | 1 | 0.01% |
| EDC0318 | 1 | 0.01% |
| EDC3445 | 1 | 0.01% |
| EDC3162 | 1 | 0.01% |
| EDC3919 | 1 | 0.01% |
| EDC2568 | 1 | 0.01% |
| EDC0761 | 1 | 0.01% |
| EDC3823 | 1 | 0.01% |
| EDC5106 | 1 | 0.01% |
| EDC4644 | 1 | 0.01% |
| EDC2721 | 1 | 0.01% |
| EDC1152 | 1 | 0.01% |
| EDC3417 | 1 | 0.01% |
| EDC3938 | 1 | 0.01% |
| EDC4564 | 1 | 0.01% |
| EDC1233 | 1 | 0.01% |
| EDC0187 | 1 | 0.01% |
| EDC4104 | 1 | 0.01% |
| EDC3836 | 1 | 0.01% |
| EDC1327 | 1 | 0.01% |
| EDC4952 | 1 | 0.01% |
| EDC4817 | 1 | 0.01% |
| EDC3818 | 1 | 0.01% |
| EDC1809 | 1 | 0.01% |
| EDC2802 | 1 | 0.01% |
| EDC0708 | 1 | 0.01% |
| EDC5260 | 1 | 0.01% |
| EDC4061 | 1 | 0.01% |
| EDC3196 | 1 | 0.01% |
| EDC3218 | 1 | 0.01% |
| EDC3359 | 1 | 0.01% |
| EDC3299 | 1 | 0.01% |
| EDC0859 | 1 | 0.01% |
| EDC3494 | 1 | 0.01% |
| EDC4464 | 1 | 0.01% |
| EDC1222 | 1 | 0.01% |
| EDC4397 | 1 | 0.01% |
| EDC1475 | 1 | 0.01% |
| EDC3067 | 1 | 0.01% |
| EDC3298 | 1 | 0.01% |
| EDC5182 | 1 | 0.01% |
| EDC5333 | 1 | 0.01% |
| EDC3334 | 1 | 0.01% |
| EDC1053 | 1 | 0.01% |
| EDC2327 | 1 | 0.01% |
| EDC5134 | 1 | 0.01% |
| EDC3671 | 1 | 0.01% |
| EDC1396 | 1 | 0.01% |
| EDC4968 | 1 | 0.01% |
| EDC0837 | 1 | 0.01% |
| EDC2369 | 1 | 0.01% |
| EDC3227 | 1 | 0.01% |
| EDC4514 | 1 | 0.01% |
| EDC3850 | 1 | 0.01% |
| EDC0467 | 1 | 0.01% |
| EDC3419 | 1 | 0.01% |
| EDC5286 | 1 | 0.01% |
| EDC1274 | 1 | 0.01% |
| EDC2928 | 1 | 0.01% |
| EDC4764 | 1 | 0.01% |
| EDC4052 | 1 | 0.01% |
| EDC1406 | 1 | 0.01% |
| EDC4914 | 1 | 0.01% |
| EDC4064 | 1 | 0.01% |
| EDC0093 | 1 | 0.01% |
| EDC2143 | 1 | 0.01% |
| EDC1973 | 1 | 0.01% |
| EDC1452 | 1 | 0.01% |
| EDC2916 | 1 | 0.01% |
| EDC5206 | 1 | 0.01% |
| EDC0629 | 1 | 0.01% |
| EDC5070 | 1 | 0.01% |
| EDC3743 | 1 | 0.01% |
| EDC0630 | 1 | 0.01% |
| EDC3507 | 1 | 0.01% |
| EDC1442 | 1 | 0.01% |
| EDC3733 | 1 | 0.01% |
| EDC4205 | 1 | 0.01% |
| EDC1196 | 1 | 0.01% |
| EDC0031 | 1 | 0.01% |
| EDC1975 | 1 | 0.01% |
| EDC0634 | 1 | 0.01% |
| EDC4116 | 1 | 0.01% |
| EDC3090 | 1 | 0.01% |
| EDC4037 | 1 | 0.01% |
| EDC3363 | 1 | 0.01% |
| EDC5201 | 1 | 0.01% |
| EDC3449 | 1 | 0.01% |
| EDC0382 | 1 | 0.01% |
| EDC1936 | 1 | 0.01% |
| EDC2333 | 1 | 0.01% |
| EDC3058 | 1 | 0.01% |
| EDC2262 | 1 | 0.01% |
| EDC1951 | 1 | 0.01% |
| EDC0952 | 1 | 0.01% |
| EDC2255 | 1 | 0.01% |
| EDC1990 | 1 | 0.01% |
| EDC4234 | 1 | 0.01% |
| EDC4430 | 1 | 0.01% |
| EDC3006 | 1 | 0.01% |
| EDC4696 | 1 | 0.01% |
| EDC3740 | 1 | 0.01% |
| EDC2335 | 1 | 0.01% |
| EDC5168 | 1 | 0.01% |
| EDC5153 | 1 | 0.01% |
| EDC5067 | 1 | 0.01% |
| EDC2524 | 1 | 0.01% |
| EDC5071 | 1 | 0.01% |
| EDC1748 | 1 | 0.01% |
| EDC0470 | 1 | 0.01% |
| EDC0354 | 1 | 0.01% |
| EDC5065 | 1 | 0.01% |
| EDC1551 | 1 | 0.01% |
| EDC0645 | 1 | 0.01% |
| EDC5102 | 1 | 0.01% |
| EDC4592 | 1 | 0.01% |
| EDC4320 | 1 | 0.01% |
| EDC3233 | 1 | 0.01% |
| EDC4408 | 1 | 0.01% |
| EDC2815 | 1 | 0.01% |
| EDC0015 | 1 | 0.01% |
| EDC3980 | 1 | 0.01% |
| EDC2604 | 1 | 0.01% |
| EDC4638 | 1 | 0.01% |
| EDC1387 | 1 | 0.01% |
| EDC1570 | 1 | 0.01% |
| EDC2572 | 1 | 0.01% |
| EDC0842 | 1 | 0.01% |
| EDC3964 | 1 | 0.01% |
| EDC2184 | 1 | 0.01% |
| EDC1982 | 1 | 0.01% |
| EDC4920 | 1 | 0.01% |
| EDC4161 | 1 | 0.01% |
| EDC3681 | 1 | 0.01% |
| EDC4455 | 1 | 0.01% |
| EDC4207 | 1 | 0.01% |
| EDC0895 | 1 | 0.01% |
| EDC3786 | 1 | 0.01% |
| EDC1464 | 1 | 0.01% |
| EDC3088 | 1 | 0.01% |
| EDC3666 | 1 | 0.01% |
| EDC4824 | 1 | 0.01% |
| EDC4668 | 1 | 0.01% |
| EDC4242 | 1 | 0.01% |
| EDC5274 | 1 | 0.01% |
| EDC3615 | 1 | 0.01% |
| EDC1086 | 1 | 0.01% |
| EDC4991 | 1 | 0.01% |
| EDC0866 | 1 | 0.01% |
| EDC4910 | 1 | 0.01% |
| EDC2379 | 1 | 0.01% |
| EDC4731 | 1 | 0.01% |
| EDC2882 | 1 | 0.01% |
| EDC4343 | 1 | 0.01% |
| EDC2044 | 1 | 0.01% |
| EDC0663 | 1 | 0.01% |
| EDC0499 | 1 | 0.01% |
| EDC1777 | 1 | 0.01% |
| EDC0273 | 1 | 0.01% |
| EDC0144 | 1 | 0.01% |
| EDC0954 | 1 | 0.01% |
| EDC2943 | 1 | 0.01% |
| EDC4210 | 1 | 0.01% |
| EDC0843 | 1 | 0.01% |
| EDC4767 | 1 | 0.01% |
| EDC1207 | 1 | 0.01% |
| EDC0858 | 1 | 0.01% |
| EDC1739 | 1 | 0.01% |
| EDC0869 | 1 | 0.01% |
| EDC0997 | 1 | 0.01% |
| EDC2486 | 1 | 0.01% |
| EDC4756 | 1 | 0.01% |
| EDC0961 | 1 | 0.01% |
| EDC1320 | 1 | 0.01% |
| EDC1859 | 1 | 0.01% |
| EDC3133 | 1 | 0.01% |
| EDC1594 | 1 | 0.01% |
| EDC0484 | 1 | 0.01% |
| EDC3514 | 1 | 0.01% |
| EDC4271 | 1 | 0.01% |
| EDC4761 | 1 | 0.01% |
| EDC4030 | 1 | 0.01% |
| EDC2662 | 1 | 0.01% |
| EDC1727 | 1 | 0.01% |
| EDC4656 | 1 | 0.01% |
| EDC0394 | 1 | 0.01% |
| EDC0460 | 1 | 0.01% |
| EDC3658 | 1 | 0.01% |
| EDC3368 | 1 | 0.01% |
| EDC2336 | 1 | 0.01% |
| EDC1231 | 1 | 0.01% |
| EDC0271 | 1 | 0.01% |
| EDC5076 | 1 | 0.01% |
| EDC2189 | 1 | 0.01% |
| EDC0237 | 1 | 0.01% |
| EDC2412 | 1 | 0.01% |
| EDC1851 | 1 | 0.01% |
| EDC3189 | 1 | 0.01% |
| EDC1984 | 1 | 0.01% |
| EDC1490 | 1 | 0.01% |
| EDC2925 | 1 | 0.01% |
| EDC0875 | 1 | 0.01% |
| EDC1850 | 1 | 0.01% |
| EDC3978 | 1 | 0.01% |
| EDC3264 | 1 | 0.01% |
| EDC2931 | 1 | 0.01% |
| EDC4468 | 1 | 0.01% |
| EDC3438 | 1 | 0.01% |
| EDC1758 | 1 | 0.01% |
| EDC5198 | 1 | 0.01% |
| EDC4427 | 1 | 0.01% |
| EDC2774 | 1 | 0.01% |
| EDC1239 | 1 | 0.01% |
| EDC3030 | 1 | 0.01% |
| EDC4752 | 1 | 0.01% |
| EDC2146 | 1 | 0.01% |
| EDC0087 | 1 | 0.01% |
| EDC4788 | 1 | 0.01% |
| EDC1034 | 1 | 0.01% |
| EDC4394 | 1 | 0.01% |
| EDC4634 | 1 | 0.01% |
| EDC0358 | 1 | 0.01% |
| EDC4955 | 1 | 0.01% |
| EDC2203 | 1 | 0.01% |
| EDC2567 | 1 | 0.01% |
| EDC4513 | 1 | 0.01% |
| EDC0183 | 1 | 0.01% |
| EDC3167 | 1 | 0.01% |
| EDC1138 | 1 | 0.01% |
| EDC4182 | 1 | 0.01% |
| EDC3198 | 1 | 0.01% |
| EDC0126 | 1 | 0.01% |
| EDC2557 | 1 | 0.01% |
| EDC0025 | 1 | 0.01% |
| EDC1949 | 1 | 0.01% |
| EDC0671 | 1 | 0.01% |
| EDC4020 | 1 | 0.01% |
| EDC4958 | 1 | 0.01% |
| EDC4009 | 1 | 0.01% |
| EDC4998 | 1 | 0.01% |
| EDC0985 | 1 | 0.01% |
| EDC1701 | 1 | 0.01% |
| EDC4488 | 1 | 0.01% |
| EDC4548 | 1 | 0.01% |
| EDC4395 | 1 | 0.01% |
| EDC0868 | 1 | 0.01% |
| EDC5029 | 1 | 0.01% |
| EDC0596 | 1 | 0.01% |
| EDC4378 | 1 | 0.01% |
| EDC0018 | 1 | 0.01% |
| EDC4850 | 1 | 0.01% |
| EDC3625 | 1 | 0.01% |
| EDC0143 | 1 | 0.01% |
| EDC0485 | 1 | 0.01% |
| EDC4618 | 1 | 0.01% |
| EDC2332 | 1 | 0.01% |
| EDC2201 | 1 | 0.01% |
| EDC0656 | 1 | 0.01% |
| EDC5051 | 1 | 0.01% |
| EDC1473 | 1 | 0.01% |
| EDC0804 | 1 | 0.01% |
| EDC4536 | 1 | 0.01% |
| EDC2683 | 1 | 0.01% |
| EDC5178 | 1 | 0.01% |
| EDC4255 | 1 | 0.01% |
| EDC5188 | 1 | 0.01% |
| EDC5092 | 1 | 0.01% |
| EDC3185 | 1 | 0.01% |
| EDC3120 | 1 | 0.01% |
| EDC1012 | 1 | 0.01% |
| EDC2613 | 1 | 0.01% |
| EDC5273 | 1 | 0.01% |
| EDC4858 | 1 | 0.01% |
| EDC3395 | 1 | 0.01% |
| EDC0069 | 1 | 0.01% |
| EDC1579 | 1 | 0.01% |
| EDC4553 | 1 | 0.01% |
| EDC4537 | 1 | 0.01% |
| EDC4474 | 1 | 0.01% |
| EDC3200 | 1 | 0.01% |
| EDC1335 | 1 | 0.01% |
| EDC0568 | 1 | 0.01% |
| EDC1313 | 1 | 0.01% |
| EDC2029 | 1 | 0.01% |
| EDC3567 | 1 | 0.01% |
| EDC3565 | 1 | 0.01% |
| EDC4918 | 1 | 0.01% |
| EDC4781 | 1 | 0.01% |
| EDC0441 | 1 | 0.01% |
| EDC2153 | 1 | 0.01% |
| EDC0637 | 1 | 0.01% |
| EDC3355 | 1 | 0.01% |
| EDC0452 | 1 | 0.01% |
| EDC1649 | 1 | 0.01% |
| EDC4483 | 1 | 0.01% |
| EDC0350 | 1 | 0.01% |
| EDC4922 | 1 | 0.01% |
| EDC4967 | 1 | 0.01% |
| EDC3533 | 1 | 0.01% |
| EDC3620 | 1 | 0.01% |
| EDC0571 | 1 | 0.01% |
| EDC4848 | 1 | 0.01% |
| EDC1843 | 1 | 0.01% |
| EDC0932 | 1 | 0.01% |
| EDC0252 | 1 | 0.01% |
| EDC3909 | 1 | 0.01% |
| EDC4557 | 1 | 0.01% |
| EDC1961 | 1 | 0.01% |
| EDC1823 | 1 | 0.01% |
| EDC4112 | 1 | 0.01% |
| EDC4056 | 1 | 0.01% |
| EDC5196 | 1 | 0.01% |
| EDC0947 | 1 | 0.01% |
| EDC4288 | 1 | 0.01% |
| EDC0839 | 1 | 0.01% |
| EDC5282 | 1 | 0.01% |
| EDC0980 | 1 | 0.01% |
| EDC1266 | 1 | 0.01% |
| EDC4418 | 1 | 0.01% |
| EDC0032 | 1 | 0.01% |
| EDC0055 | 1 | 0.01% |
| EDC3808 | 1 | 0.01% |
| EDC5000 | 1 | 0.01% |
| EDC3999 | 1 | 0.01% |
| EDC1636 | 1 | 0.01% |
| EDC3791 | 1 | 0.01% |
| EDC4704 | 1 | 0.01% |
| EDC1824 | 1 | 0.01% |
| EDC0434 | 1 | 0.01% |
| EDC0133 | 1 | 0.01% |
| EDC3128 | 1 | 0.01% |
| EDC1907 | 1 | 0.01% |
| EDC0562 | 1 | 0.01% |
| EDC5042 | 1 | 0.01% |
| EDC4708 | 1 | 0.01% |
| EDC0209 | 1 | 0.01% |
| EDC3317 | 1 | 0.01% |
| EDC1361 | 1 | 0.01% |
| EDC4550 | 1 | 0.01% |
| EDC3290 | 1 | 0.01% |
| EDC3668 | 1 | 0.01% |
| EDC3859 | 1 | 0.01% |
| EDC4736 | 1 | 0.01% |
| EDC3552 | 1 | 0.01% |
| EDC4802 | 1 | 0.01% |
| EDC4093 | 1 | 0.01% |
| EDC5264 | 1 | 0.01% |
| EDC1216 | 1 | 0.01% |
| EDC3257 | 1 | 0.01% |
| EDC4937 | 1 | 0.01% |
| EDC2981 | 1 | 0.01% |
| EDC2753 | 1 | 0.01% |
| EDC0963 | 1 | 0.01% |
| EDC4686 | 1 | 0.01% |
| EDC1677 | 1 | 0.01% |
| EDC3406 | 1 | 0.01% |
| EDC4511 | 1 | 0.01% |
| EDC1371 | 1 | 0.01% |
| EDC4057 | 1 | 0.01% |
| EDC4938 | 1 | 0.01% |
| EDC1009 | 1 | 0.01% |
| EDC4225 | 1 | 0.01% |
| EDC4774 | 1 | 0.01% |
| EDC0802 | 1 | 0.01% |
| EDC0719 | 1 | 0.01% |
| EDC0502 | 1 | 0.01% |
| EDC3800 | 1 | 0.01% |
| EDC4805 | 1 | 0.01% |
| EDC2304 | 1 | 0.01% |
| EDC4714 | 1 | 0.01% |
| EDC2580 | 1 | 0.01% |
| EDC3283 | 1 | 0.01% |
| EDC0638 | 1 | 0.01% |
| EDC5057 | 1 | 0.01% |
| EDC0487 | 1 | 0.01% |
| EDC1362 | 1 | 0.01% |
| EDC3742 | 1 | 0.01% |
| EDC2826 | 1 | 0.01% |
| EDC4113 | 1 | 0.01% |
| EDC2507 | 1 | 0.01% |
| EDC1901 | 1 | 0.01% |
| EDC0411 | 1 | 0.01% |
| EDC5157 | 1 | 0.01% |
| EDC2495 | 1 | 0.01% |
| EDC5187 | 1 | 0.01% |
| EDC0072 | 1 | 0.01% |
| EDC0527 | 1 | 0.01% |
| EDC0723 | 1 | 0.01% |
| EDC3594 | 1 | 0.01% |
| EDC4732 | 1 | 0.01% |
| EDC3794 | 1 | 0.01% |
| EDC2907 | 1 | 0.01% |
| EDC4339 | 1 | 0.01% |
| EDC2646 | 1 | 0.01% |
| EDC4904 | 1 | 0.01% |
| EDC3842 | 1 | 0.01% |
| EDC3917 | 1 | 0.01% |
| EDC0919 | 1 | 0.01% |
| EDC2660 | 1 | 0.01% |
| EDC4213 | 1 | 0.01% |
| EDC1453 | 1 | 0.01% |
| EDC5100 | 1 | 0.01% |
| EDC2176 | 1 | 0.01% |
| EDC0748 | 1 | 0.01% |
| EDC0304 | 1 | 0.01% |
| EDC1566 | 1 | 0.01% |
| EDC2086 | 1 | 0.01% |
| EDC3214 | 1 | 0.01% |
| EDC4131 | 1 | 0.01% |
| EDC3970 | 1 | 0.01% |
| EDC1337 | 1 | 0.01% |
| EDC2847 | 1 | 0.01% |
| EDC2919 | 1 | 0.01% |
| EDC2511 | 1 | 0.01% |
| EDC2927 | 1 | 0.01% |
| EDC4790 | 1 | 0.01% |
| EDC5226 | 1 | 0.01% |
| EDC1918 | 1 | 0.01% |
| EDC4384 | 1 | 0.01% |
| EDC2886 | 1 | 0.01% |
| EDC5120 | 1 | 0.01% |
| EDC5216 | 1 | 0.01% |
| EDC2805 | 1 | 0.01% |
| EDC1021 | 1 | 0.01% |
| EDC2978 | 1 | 0.01% |
| EDC1411 | 1 | 0.01% |
| EDC1883 | 1 | 0.01% |
| EDC1167 | 1 | 0.01% |
| EDC3145 | 1 | 0.01% |
| EDC1712 | 1 | 0.01% |
| EDC2340 | 1 | 0.01% |
| EDC1599 | 1 | 0.01% |
| EDC0398 | 1 | 0.01% |
| EDC2003 | 1 | 0.01% |
| EDC3732 | 1 | 0.01% |
| EDC4406 | 1 | 0.01% |
| EDC2235 | 1 | 0.01% |
| EDC1500 | 1 | 0.01% |
| EDC2663 | 1 | 0.01% |
| EDC4560 | 1 | 0.01% |
| EDC4026 | 1 | 0.01% |
| EDC0206 | 1 | 0.01% |
| EDC0763 | 1 | 0.01% |
| EDC2216 | 1 | 0.01% |
| EDC2334 | 1 | 0.01% |
| EDC3478 | 1 | 0.01% |
| EDC3735 | 1 | 0.01% |
| EDC2251 | 1 | 0.01% |
| EDC0142 | 1 | 0.01% |
| EDC2467 | 1 | 0.01% |
| EDC3272 | 1 | 0.01% |
| EDC2013 | 1 | 0.01% |
| EDC2574 | 1 | 0.01% |
| EDC2924 | 1 | 0.01% |
| EDC3847 | 1 | 0.01% |
| EDC1025 | 1 | 0.01% |
| EDC0207 | 1 | 0.01% |
| EDC3440 | 1 | 0.01% |
| EDC3832 | 1 | 0.01% |
| EDC3040 | 1 | 0.01% |
| EDC2329 | 1 | 0.01% |
| EDC4000 | 1 | 0.01% |
| EDC2005 | 1 | 0.01% |
| EDC4151 | 1 | 0.01% |
| EDC4176 | 1 | 0.01% |
| EDC2689 | 1 | 0.01% |
| EDC2345 | 1 | 0.01% |
| EDC5218 | 1 | 0.01% |
| EDC1192 | 1 | 0.01% |
| EDC1180 | 1 | 0.01% |
| EDC2250 | 1 | 0.01% |
| EDC0051 | 1 | 0.01% |
| EDC5279 | 1 | 0.01% |
| EDC1487 | 1 | 0.01% |
| EDC3364 | 1 | 0.01% |
| EDC0347 | 1 | 0.01% |
| EDC1431 | 1 | 0.01% |
| EDC5047 | 1 | 0.01% |
| EDC5082 | 1 | 0.01% |
| EDC0171 | 1 | 0.01% |
| EDC0827 | 1 | 0.01% |
| EDC5235 | 1 | 0.01% |
| EDC2354 | 1 | 0.01% |
| EDC4666 | 1 | 0.01% |
| EDC5012 | 1 | 0.01% |
| EDC3212 | 1 | 0.01% |
| EDC3734 | 1 | 0.01% |
| EDC0988 | 1 | 0.01% |
| EDC4023 | 1 | 0.01% |
| EDC5098 | 1 | 0.01% |
| EDC3754 | 1 | 0.01% |
| EDC1704 | 1 | 0.01% |
| EDC2038 | 1 | 0.01% |
| EDC0430 | 1 | 0.01% |
| EDC4649 | 1 | 0.01% |
| EDC2433 | 1 | 0.01% |
| EDC1213 | 1 | 0.01% |
| EDC2934 | 1 | 0.01% |
| EDC4588 | 1 | 0.01% |
| EDC0311 | 1 | 0.01% |
| EDC4194 | 1 | 0.01% |
| EDC2940 | 1 | 0.01% |
| EDC0178 | 1 | 0.01% |
| EDC2376 | 1 | 0.01% |
| EDC3172 | 1 | 0.01% |
| EDC1400 | 1 | 0.01% |
| EDC4071 | 1 | 0.01% |
| EDC0899 | 1 | 0.01% |
| EDC4879 | 1 | 0.01% |
| EDC0023 | 1 | 0.01% |
| EDC4552 | 1 | 0.01% |
| EDC0834 | 1 | 0.01% |
| EDC0004 | 1 | 0.01% |
| EDC4559 | 1 | 0.01% |
| EDC0690 | 1 | 0.01% |
| EDC5130 | 1 | 0.01% |
| EDC3863 | 1 | 0.01% |
| EDC3856 | 1 | 0.01% |
| EDC1547 | 1 | 0.01% |
| EDC4355 | 1 | 0.01% |
| EDC2953 | 1 | 0.01% |
| EDC3048 | 1 | 0.01% |
| EDC1199 | 1 | 0.01% |
| EDC2233 | 1 | 0.01% |
| EDC2437 | 1 | 0.01% |
| EDC1681 | 1 | 0.01% |
| EDC3224 | 1 | 0.01% |
| EDC0498 | 1 | 0.01% |
| EDC0431 | 1 | 0.01% |
| EDC0057 | 1 | 0.01% |
| EDC3149 | 1 | 0.01% |
| EDC0846 | 1 | 0.01% |
| EDC0829 | 1 | 0.01% |
| EDC4556 | 1 | 0.01% |
| EDC4275 | 1 | 0.01% |
| EDC4450 | 1 | 0.01% |
| EDC2970 | 1 | 0.01% |
| EDC2719 | 1 | 0.01% |
| EDC0296 | 1 | 0.01% |
| EDC3327 | 1 | 0.01% |
| EDC0405 | 1 | 0.01% |
| EDC4462 | 1 | 0.01% |
| EDC0332 | 1 | 0.01% |
| EDC2393 | 1 | 0.01% |
| EDC3333 | 1 | 0.01% |
| EDC1491 | 1 | 0.01% |
| EDC3511 | 1 | 0.01% |
| EDC2559 | 1 | 0.01% |
| EDC2375 | 1 | 0.01% |
| EDC2219 | 1 | 0.01% |
| EDC4178 | 1 | 0.01% |
| EDC1382 | 1 | 0.01% |
| EDC3118 | 1 | 0.01% |
| EDC1224 | 1 | 0.01% |
| EDC4582 | 1 | 0.01% |
| EDC1190 | 1 | 0.01% |
| EDC0420 | 1 | 0.01% |
| EDC1118 | 1 | 0.01% |
| EDC3655 | 1 | 0.01% |
| EDC4062 | 1 | 0.01% |
| EDC5229 | 1 | 0.01% |
| EDC3882 | 1 | 0.01% |
| EDC0836 | 1 | 0.01% |
| EDC3085 | 1 | 0.01% |
| EDC1917 | 1 | 0.01% |
| EDC4342 | 1 | 0.01% |
| EDC0877 | 1 | 0.01% |
| EDC2545 | 1 | 0.01% |
| EDC1709 | 1 | 0.01% |
| EDC1668 | 1 | 0.01% |
| EDC4631 | 1 | 0.01% |
| EDC3017 | 1 | 0.01% |
| EDC0122 | 1 | 0.01% |
| EDC5197 | 1 | 0.01% |
| EDC5192 | 1 | 0.01% |
| EDC1397 | 1 | 0.01% |
| EDC2426 | 1 | 0.01% |
data['EDC_Type'].replace({'EDC0002':0}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 3 | 50000.0 | 0 | 1 | 1 | M0001 | CTY06-133 | EDC0885 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | EDC0565 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-073 | EDC4639 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | EDC3918 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 4 | 2500000.0 | 5 | 1 | 1 | M0001 | CTY06-133 | EDC2863 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
unused = data['EDC_Type'].loc[data['EDC_Type']!= 0]
unused
0 EDC0885
1 EDC0565
2 EDC4639
3 EDC3918
4 EDC2863
...
10495 EDC0741
10496 EDC0797
10497 EDC0587
10498 EDC0626
10499 EDC4477
Name: EDC_Type, Length: 9793, dtype: object
data.replace(unused.values, 1, inplace=True)
data
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 3 | 50000.0 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | 1 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-073 | 1 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | 1 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 4 | 2500000.0 | 5 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | 2018-12-31 15:47:34.782 | 4 | 5000000.0 | 1 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | 2018-12-31 15:47:34.782 | 4 | 4800000.0 | 0 | 1 | 1 | M0001 | CTY06-181 | 1 | OEDC0377 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | 2018-12-31 15:47:34.782 | 4 | 2500000.0 | 3 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | 2018-12-31 15:47:34.782 | 4 | 1100000.0 | 0 | 1 | 1 | M0001 | CTY06-186 | 1 | OEDC0377 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | 2018-12-31 22:11:05.961 | 2 | 102500.0 | 0 | 1 | 2 | M0597 | CTY06-171 | 1 | OEDC0633 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10500 rows × 15 columns
value_counts(data, 'Channel_ID')
| Value | Count | Percentage |
|---|---|---|
| 1 | 8,325 | 79.29% |
| 2 | 1,463 | 13.93% |
| 5 | 682 | 6.50% |
| 3 | 25 | 0.24% |
| 4 | 5 | 0.05% |
data['Channel_ID'].replace({1:1}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 3 | 50000.0 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | 1 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-073 | 1 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | 1 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 4 | 2500000.0 | 5 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
unused = data['Channel_ID'].loc[data['Channel_ID']!= 1]
unused
8 5
9 5
13 5
21 5
29 5
..
10471 5
10475 2
10492 5
10493 5
10499 2
Name: Channel_ID, Length: 2175, dtype: int64
data.replace(unused.values, 0, inplace=True)
data
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | 1 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-073 | 1 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | M0001 | CTY06-129 | 1 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | 2018-12-31 15:47:34.782 | 0 | 5000000.0 | 1 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | 2018-12-31 15:47:34.782 | 0 | 4800000.0 | 0 | 1 | 1 | M0001 | CTY06-181 | 1 | OEDC0377 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | 2018-12-31 15:47:34.782 | 0 | 2500000.0 | 0 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | 2018-12-31 15:47:34.782 | 0 | 1100000.0 | 0 | 1 | 1 | M0001 | CTY06-186 | 1 | OEDC0377 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | 2018-12-31 22:11:05.961 | 0 | 102500.0 | 0 | 1 | 0 | M0597 | CTY06-171 | 1 | OEDC0633 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10500 rows × 15 columns
value_counts(data, 'Merchant_ID')
| Value | Count | Percentage |
|---|---|---|
| M0001 | 9,037 | 86.07% |
| M0243 | 6 | 0.06% |
| M0140 | 6 | 0.06% |
| M0063 | 6 | 0.06% |
| M0100 | 6 | 0.06% |
| M0343 | 5 | 0.05% |
| M0405 | 5 | 0.05% |
| M0088 | 5 | 0.05% |
| M0187 | 5 | 0.05% |
| M0307 | 4 | 0.04% |
| M0592 | 4 | 0.04% |
| M0418 | 4 | 0.04% |
| M0200 | 4 | 0.04% |
| M0514 | 4 | 0.04% |
| M0633 | 4 | 0.04% |
| M0013 | 4 | 0.04% |
| M0323 | 4 | 0.04% |
| M0104 | 4 | 0.04% |
| M0156 | 4 | 0.04% |
| M0510 | 4 | 0.04% |
| M0081 | 4 | 0.04% |
| M0419 | 4 | 0.04% |
| M0644 | 4 | 0.04% |
| M0325 | 4 | 0.04% |
| M0350 | 4 | 0.04% |
| M0434 | 4 | 0.04% |
| M0208 | 4 | 0.04% |
| M0155 | 4 | 0.04% |
| M0202 | 4 | 0.04% |
| M0649 | 4 | 0.04% |
| M0017 | 4 | 0.04% |
| M0524 | 3 | 0.03% |
| M0447 | 3 | 0.03% |
| M0111 | 3 | 0.03% |
| M0117 | 3 | 0.03% |
| M0391 | 3 | 0.03% |
| M0093 | 3 | 0.03% |
| M0486 | 3 | 0.03% |
| M0274 | 3 | 0.03% |
| M0363 | 3 | 0.03% |
| M0084 | 3 | 0.03% |
| M0332 | 3 | 0.03% |
| M0437 | 3 | 0.03% |
| M0231 | 3 | 0.03% |
| M0511 | 3 | 0.03% |
| M0004 | 3 | 0.03% |
| M0463 | 3 | 0.03% |
| M0220 | 3 | 0.03% |
| M0618 | 3 | 0.03% |
| M0087 | 3 | 0.03% |
| M0267 | 3 | 0.03% |
| M0019 | 3 | 0.03% |
| M0234 | 3 | 0.03% |
| M0354 | 3 | 0.03% |
| M0523 | 3 | 0.03% |
| M0472 | 3 | 0.03% |
| M0153 | 3 | 0.03% |
| M0708 | 3 | 0.03% |
| M0059 | 3 | 0.03% |
| M0287 | 3 | 0.03% |
| M0014 | 3 | 0.03% |
| M0184 | 3 | 0.03% |
| M0545 | 3 | 0.03% |
| M0394 | 3 | 0.03% |
| M0058 | 3 | 0.03% |
| M0590 | 3 | 0.03% |
| M0549 | 3 | 0.03% |
| M0238 | 3 | 0.03% |
| M0565 | 3 | 0.03% |
| M0397 | 3 | 0.03% |
| M0393 | 3 | 0.03% |
| M0724 | 3 | 0.03% |
| M0168 | 3 | 0.03% |
| M0719 | 3 | 0.03% |
| M0544 | 3 | 0.03% |
| M0326 | 3 | 0.03% |
| M0141 | 3 | 0.03% |
| M0169 | 3 | 0.03% |
| M0538 | 3 | 0.03% |
| M0497 | 3 | 0.03% |
| M0008 | 3 | 0.03% |
| M0114 | 3 | 0.03% |
| M0294 | 3 | 0.03% |
| M0221 | 3 | 0.03% |
| M0467 | 3 | 0.03% |
| M0310 | 3 | 0.03% |
| M0262 | 3 | 0.03% |
| M0075 | 3 | 0.03% |
| M0588 | 3 | 0.03% |
| M0159 | 3 | 0.03% |
| M0424 | 3 | 0.03% |
| M0313 | 3 | 0.03% |
| M0413 | 3 | 0.03% |
| M0139 | 3 | 0.03% |
| M0776 | 3 | 0.03% |
| M0261 | 3 | 0.03% |
| M0764 | 3 | 0.03% |
| M0475 | 3 | 0.03% |
| M0714 | 3 | 0.03% |
| M0229 | 3 | 0.03% |
| M0687 | 3 | 0.03% |
| M0493 | 3 | 0.03% |
| M0715 | 3 | 0.03% |
| M0259 | 3 | 0.03% |
| M0197 | 3 | 0.03% |
| M0595 | 3 | 0.03% |
| M0745 | 3 | 0.03% |
| M0038 | 3 | 0.03% |
| M0795 | 3 | 0.03% |
| M0006 | 3 | 0.03% |
| M0382 | 3 | 0.03% |
| M0386 | 3 | 0.03% |
| M0839 | 3 | 0.03% |
| M0358 | 3 | 0.03% |
| M0657 | 3 | 0.03% |
| M0282 | 3 | 0.03% |
| M1025 | 2 | 0.02% |
| M0828 | 2 | 0.02% |
| M0183 | 2 | 0.02% |
| M0039 | 2 | 0.02% |
| M0620 | 2 | 0.02% |
| M0627 | 2 | 0.02% |
| M0449 | 2 | 0.02% |
| M0384 | 2 | 0.02% |
| M0471 | 2 | 0.02% |
| M0850 | 2 | 0.02% |
| M0872 | 2 | 0.02% |
| M0789 | 2 | 0.02% |
| M0579 | 2 | 0.02% |
| M0025 | 2 | 0.02% |
| M0022 | 2 | 0.02% |
| M1007 | 2 | 0.02% |
| M0666 | 2 | 0.02% |
| M0285 | 2 | 0.02% |
| M0409 | 2 | 0.02% |
| M0534 | 2 | 0.02% |
| M0451 | 2 | 0.02% |
| M0669 | 2 | 0.02% |
| M0533 | 2 | 0.02% |
| M0406 | 2 | 0.02% |
| M0757 | 2 | 0.02% |
| M0580 | 2 | 0.02% |
| M0360 | 2 | 0.02% |
| M0042 | 2 | 0.02% |
| M0346 | 2 | 0.02% |
| M0170 | 2 | 0.02% |
| M0124 | 2 | 0.02% |
| M0547 | 2 | 0.02% |
| M0468 | 2 | 0.02% |
| M0148 | 2 | 0.02% |
| M0302 | 2 | 0.02% |
| M0816 | 2 | 0.02% |
| M0143 | 2 | 0.02% |
| M0045 | 2 | 0.02% |
| M0304 | 2 | 0.02% |
| M0503 | 2 | 0.02% |
| M0256 | 2 | 0.02% |
| M0309 | 2 | 0.02% |
| M0665 | 2 | 0.02% |
| M0494 | 2 | 0.02% |
| M0348 | 2 | 0.02% |
| M0893 | 2 | 0.02% |
| M0837 | 2 | 0.02% |
| M0341 | 2 | 0.02% |
| M0536 | 2 | 0.02% |
| M0732 | 2 | 0.02% |
| M0048 | 2 | 0.02% |
| M0272 | 2 | 0.02% |
| M0833 | 2 | 0.02% |
| M0178 | 2 | 0.02% |
| M0366 | 2 | 0.02% |
| M0530 | 2 | 0.02% |
| M0237 | 2 | 0.02% |
| M0336 | 2 | 0.02% |
| M0079 | 2 | 0.02% |
| M0012 | 2 | 0.02% |
| M0277 | 2 | 0.02% |
| M0853 | 2 | 0.02% |
| M0268 | 2 | 0.02% |
| M0137 | 2 | 0.02% |
| M0314 | 2 | 0.02% |
| M0328 | 2 | 0.02% |
| M0445 | 2 | 0.02% |
| M0191 | 2 | 0.02% |
| M0072 | 2 | 0.02% |
| M0320 | 2 | 0.02% |
| M0851 | 2 | 0.02% |
| M0707 | 2 | 0.02% |
| M0790 | 2 | 0.02% |
| M0086 | 2 | 0.02% |
| M0185 | 2 | 0.02% |
| M0319 | 2 | 0.02% |
| M0249 | 2 | 0.02% |
| M0367 | 2 | 0.02% |
| M1084 | 2 | 0.02% |
| M0980 | 2 | 0.02% |
| M0308 | 2 | 0.02% |
| M0298 | 2 | 0.02% |
| M0611 | 2 | 0.02% |
| M0832 | 2 | 0.02% |
| M0353 | 2 | 0.02% |
| M0501 | 2 | 0.02% |
| M0110 | 2 | 0.02% |
| M0689 | 2 | 0.02% |
| M0891 | 2 | 0.02% |
| M0009 | 2 | 0.02% |
| M0462 | 2 | 0.02% |
| M0404 | 2 | 0.02% |
| M0216 | 2 | 0.02% |
| M0922 | 2 | 0.02% |
| M0067 | 2 | 0.02% |
| M0460 | 2 | 0.02% |
| M0821 | 2 | 0.02% |
| M0123 | 2 | 0.02% |
| M0563 | 2 | 0.02% |
| M0160 | 2 | 0.02% |
| M0091 | 2 | 0.02% |
| M0635 | 2 | 0.02% |
| M0513 | 2 | 0.02% |
| M1012 | 2 | 0.02% |
| M0619 | 2 | 0.02% |
| M0603 | 2 | 0.02% |
| M0988 | 2 | 0.02% |
| M1060 | 2 | 0.02% |
| M0147 | 2 | 0.02% |
| M0292 | 2 | 0.02% |
| M0134 | 2 | 0.02% |
| M0018 | 2 | 0.02% |
| M0352 | 2 | 0.02% |
| M0260 | 2 | 0.02% |
| M0275 | 2 | 0.02% |
| M0071 | 2 | 0.02% |
| M0650 | 2 | 0.02% |
| M0317 | 2 | 0.02% |
| M0648 | 2 | 0.02% |
| M0257 | 2 | 0.02% |
| M0543 | 2 | 0.02% |
| M0196 | 2 | 0.02% |
| M0312 | 2 | 0.02% |
| M0696 | 2 | 0.02% |
| M0504 | 2 | 0.02% |
| M0740 | 2 | 0.02% |
| M1049 | 2 | 0.02% |
| M0246 | 2 | 0.02% |
| M0454 | 2 | 0.02% |
| M0604 | 2 | 0.02% |
| M0151 | 2 | 0.02% |
| M0021 | 2 | 0.02% |
| M0333 | 2 | 0.02% |
| M0975 | 2 | 0.02% |
| M0263 | 2 | 0.02% |
| M0456 | 2 | 0.02% |
| M0654 | 2 | 0.02% |
| M0609 | 2 | 0.02% |
| M0699 | 2 | 0.02% |
| M0372 | 2 | 0.02% |
| M0817 | 2 | 0.02% |
| M0023 | 2 | 0.02% |
| M0024 | 2 | 0.02% |
| M0094 | 2 | 0.02% |
| M0852 | 2 | 0.02% |
| M0450 | 2 | 0.02% |
| M0967 | 2 | 0.02% |
| M0729 | 2 | 0.02% |
| M0421 | 2 | 0.02% |
| M0917 | 2 | 0.02% |
| M0435 | 2 | 0.02% |
| M0062 | 2 | 0.02% |
| M0171 | 2 | 0.02% |
| M0632 | 2 | 0.02% |
| M0509 | 2 | 0.02% |
| M0057 | 2 | 0.02% |
| M0797 | 2 | 0.02% |
| M0517 | 2 | 0.02% |
| M0158 | 2 | 0.02% |
| M0389 | 2 | 0.02% |
| M0327 | 2 | 0.02% |
| M0402 | 2 | 0.02% |
| M0379 | 2 | 0.02% |
| M0481 | 2 | 0.02% |
| M0181 | 2 | 0.02% |
| M0743 | 2 | 0.02% |
| M0783 | 2 | 0.02% |
| M0571 | 2 | 0.02% |
| M0362 | 2 | 0.02% |
| M0663 | 2 | 0.02% |
| M0875 | 2 | 0.02% |
| M1022 | 2 | 0.02% |
| M0315 | 2 | 0.02% |
| M0780 | 2 | 0.02% |
| M0361 | 2 | 0.02% |
| M0845 | 2 | 0.02% |
| M0281 | 2 | 0.02% |
| M0559 | 2 | 0.02% |
| M0505 | 2 | 0.02% |
| M0702 | 2 | 0.02% |
| M0218 | 2 | 0.02% |
| M0541 | 2 | 0.02% |
| M0280 | 2 | 0.02% |
| M0428 | 2 | 0.02% |
| M0020 | 2 | 0.02% |
| M0398 | 2 | 0.02% |
| M0005 | 2 | 0.02% |
| M0484 | 2 | 0.02% |
| M0115 | 2 | 0.02% |
| M0677 | 2 | 0.02% |
| M0801 | 2 | 0.02% |
| M0763 | 2 | 0.02% |
| M0121 | 2 | 0.02% |
| M0690 | 2 | 0.02% |
| M0756 | 2 | 0.02% |
| M0892 | 2 | 0.02% |
| M0112 | 2 | 0.02% |
| M0193 | 2 | 0.02% |
| M0125 | 2 | 0.02% |
| M0368 | 2 | 0.02% |
| M0299 | 2 | 0.02% |
| M0936 | 2 | 0.02% |
| M0754 | 2 | 0.02% |
| M0383 | 2 | 0.02% |
| M0050 | 2 | 0.02% |
| M0829 | 2 | 0.02% |
| M0070 | 2 | 0.02% |
| M0250 | 2 | 0.02% |
| M0189 | 2 | 0.02% |
| M0138 | 2 | 0.02% |
| M0876 | 2 | 0.02% |
| M0805 | 2 | 0.02% |
| M0396 | 2 | 0.02% |
| M0378 | 2 | 0.02% |
| M0417 | 2 | 0.02% |
| M0136 | 2 | 0.02% |
| M0515 | 2 | 0.02% |
| M1114 | 1 | 0.01% |
| M1039 | 1 | 0.01% |
| M0999 | 1 | 0.01% |
| M0520 | 1 | 0.01% |
| M0064 | 1 | 0.01% |
| M1109 | 1 | 0.01% |
| M0190 | 1 | 0.01% |
| M0264 | 1 | 0.01% |
| M0629 | 1 | 0.01% |
| M0295 | 1 | 0.01% |
| M0914 | 1 | 0.01% |
| M0920 | 1 | 0.01% |
| M0926 | 1 | 0.01% |
| M0203 | 1 | 0.01% |
| M0930 | 1 | 0.01% |
| M0660 | 1 | 0.01% |
| M0791 | 1 | 0.01% |
| M0992 | 1 | 0.01% |
| M0150 | 1 | 0.01% |
| M0375 | 1 | 0.01% |
| M0906 | 1 | 0.01% |
| M0862 | 1 | 0.01% |
| M0923 | 1 | 0.01% |
| M0997 | 1 | 0.01% |
| M0452 | 1 | 0.01% |
| M0728 | 1 | 0.01% |
| M1048 | 1 | 0.01% |
| M0955 | 1 | 0.01% |
| M0116 | 1 | 0.01% |
| M0551 | 1 | 0.01% |
| M0761 | 1 | 0.01% |
| M0961 | 1 | 0.01% |
| M0010 | 1 | 0.01% |
| M0126 | 1 | 0.01% |
| M0427 | 1 | 0.01% |
| M0446 | 1 | 0.01% |
| M0003 | 1 | 0.01% |
| M0399 | 1 | 0.01% |
| M0172 | 1 | 0.01% |
| M0877 | 1 | 0.01% |
| M0400 | 1 | 0.01% |
| M1035 | 1 | 0.01% |
| M0583 | 1 | 0.01% |
| M0624 | 1 | 0.01% |
| M0374 | 1 | 0.01% |
| M0971 | 1 | 0.01% |
| M0830 | 1 | 0.01% |
| M0179 | 1 | 0.01% |
| M0502 | 1 | 0.01% |
| M0132 | 1 | 0.01% |
| M0940 | 1 | 0.01% |
| M0972 | 1 | 0.01% |
| M0258 | 1 | 0.01% |
| M0807 | 1 | 0.01% |
| M0737 | 1 | 0.01% |
| M0739 | 1 | 0.01% |
| M0297 | 1 | 0.01% |
| M0469 | 1 | 0.01% |
| M0466 | 1 | 0.01% |
| M0786 | 1 | 0.01% |
| M0356 | 1 | 0.01% |
| M0929 | 1 | 0.01% |
| M0029 | 1 | 0.01% |
| M0192 | 1 | 0.01% |
| M0482 | 1 | 0.01% |
| M0919 | 1 | 0.01% |
| M0918 | 1 | 0.01% |
| M0276 | 1 | 0.01% |
| M0802 | 1 | 0.01% |
| M0686 | 1 | 0.01% |
| M1013 | 1 | 0.01% |
| M1004 | 1 | 0.01% |
| M0823 | 1 | 0.01% |
| M0762 | 1 | 0.01% |
| M0403 | 1 | 0.01% |
| M0230 | 1 | 0.01% |
| M1085 | 1 | 0.01% |
| M0144 | 1 | 0.01% |
| M0080 | 1 | 0.01% |
| M0431 | 1 | 0.01% |
| M0458 | 1 | 0.01% |
| M0827 | 1 | 0.01% |
| M0641 | 1 | 0.01% |
| M0645 | 1 | 0.01% |
| M0938 | 1 | 0.01% |
| M0182 | 1 | 0.01% |
| M0443 | 1 | 0.01% |
| M0478 | 1 | 0.01% |
| M0300 | 1 | 0.01% |
| M1119 | 1 | 0.01% |
| M0364 | 1 | 0.01% |
| M0895 | 1 | 0.01% |
| M0002 | 1 | 0.01% |
| M0810 | 1 | 0.01% |
| M0573 | 1 | 0.01% |
| M0718 | 1 | 0.01% |
| M0046 | 1 | 0.01% |
| M1094 | 1 | 0.01% |
| M0539 | 1 | 0.01% |
| M1040 | 1 | 0.01% |
| M0291 | 1 | 0.01% |
| M0681 | 1 | 0.01% |
| M0751 | 1 | 0.01% |
| M0808 | 1 | 0.01% |
| M0826 | 1 | 0.01% |
| M0857 | 1 | 0.01% |
| M0388 | 1 | 0.01% |
| M0792 | 1 | 0.01% |
| M0990 | 1 | 0.01% |
| M0924 | 1 | 0.01% |
| M0752 | 1 | 0.01% |
| M0978 | 1 | 0.01% |
| M0744 | 1 | 0.01% |
| M1073 | 1 | 0.01% |
| M0723 | 1 | 0.01% |
| M0149 | 1 | 0.01% |
| M0215 | 1 | 0.01% |
| M0154 | 1 | 0.01% |
| M1058 | 1 | 0.01% |
| M0480 | 1 | 0.01% |
| M0305 | 1 | 0.01% |
| M0847 | 1 | 0.01% |
| M0052 | 1 | 0.01% |
| M0896 | 1 | 0.01% |
| M0376 | 1 | 0.01% |
| M0954 | 1 | 0.01% |
| M0240 | 1 | 0.01% |
| M0741 | 1 | 0.01% |
| M0145 | 1 | 0.01% |
| M0390 | 1 | 0.01% |
| M1082 | 1 | 0.01% |
| M0638 | 1 | 0.01% |
| M0835 | 1 | 0.01% |
| M0491 | 1 | 0.01% |
| M0442 | 1 | 0.01% |
| M0271 | 1 | 0.01% |
| M0204 | 1 | 0.01% |
| M0630 | 1 | 0.01% |
| M0054 | 1 | 0.01% |
| M0564 | 1 | 0.01% |
| M1020 | 1 | 0.01% |
| M0765 | 1 | 0.01% |
| M0040 | 1 | 0.01% |
| M0772 | 1 | 0.01% |
| M0444 | 1 | 0.01% |
| M0567 | 1 | 0.01% |
| M0357 | 1 | 0.01% |
| M0490 | 1 | 0.01% |
| M1052 | 1 | 0.01% |
| M0041 | 1 | 0.01% |
| M0107 | 1 | 0.01% |
| M0371 | 1 | 0.01% |
| M0927 | 1 | 0.01% |
| M0788 | 1 | 0.01% |
| M0911 | 1 | 0.01% |
| M0198 | 1 | 0.01% |
| M0982 | 1 | 0.01% |
| M0908 | 1 | 0.01% |
| M1032 | 1 | 0.01% |
| M0099 | 1 | 0.01% |
| M0974 | 1 | 0.01% |
| M0127 | 1 | 0.01% |
| M0670 | 1 | 0.01% |
| M0948 | 1 | 0.01% |
| M0613 | 1 | 0.01% |
| M0537 | 1 | 0.01% |
| M0207 | 1 | 0.01% |
| M0912 | 1 | 0.01% |
| M1077 | 1 | 0.01% |
| M0993 | 1 | 0.01% |
| M0774 | 1 | 0.01% |
| M0146 | 1 | 0.01% |
| M0252 | 1 | 0.01% |
| M0606 | 1 | 0.01% |
| M0683 | 1 | 0.01% |
| M0652 | 1 | 0.01% |
| M0984 | 1 | 0.01% |
| M0864 | 1 | 0.01% |
| M0453 | 1 | 0.01% |
| M0129 | 1 | 0.01% |
| M0301 | 1 | 0.01% |
| M0639 | 1 | 0.01% |
| M1099 | 1 | 0.01% |
| M0848 | 1 | 0.01% |
| M0889 | 1 | 0.01% |
| M0037 | 1 | 0.01% |
| M0903 | 1 | 0.01% |
| M0199 | 1 | 0.01% |
| M0697 | 1 | 0.01% |
| M0904 | 1 | 0.01% |
| M0102 | 1 | 0.01% |
| M0284 | 1 | 0.01% |
| M0722 | 1 | 0.01% |
| M0642 | 1 | 0.01% |
| M0420 | 1 | 0.01% |
| M1087 | 1 | 0.01% |
| M0142 | 1 | 0.01% |
| M1104 | 1 | 0.01% |
| M0508 | 1 | 0.01% |
| M0939 | 1 | 0.01% |
| M0615 | 1 | 0.01% |
| M0700 | 1 | 0.01% |
| M1029 | 1 | 0.01% |
| M0130 | 1 | 0.01% |
| M1033 | 1 | 0.01% |
| M0858 | 1 | 0.01% |
| M0066 | 1 | 0.01% |
| M0996 | 1 | 0.01% |
| M0255 | 1 | 0.01% |
| M1090 | 1 | 0.01% |
| M0174 | 1 | 0.01% |
| M1000 | 1 | 0.01% |
| M0748 | 1 | 0.01% |
| M0043 | 1 | 0.01% |
| M0679 | 1 | 0.01% |
| M0989 | 1 | 0.01% |
| M0245 | 1 | 0.01% |
| M1095 | 1 | 0.01% |
| M0076 | 1 | 0.01% |
| M0560 | 1 | 0.01% |
| M0986 | 1 | 0.01% |
| M0881 | 1 | 0.01% |
| M1070 | 1 | 0.01% |
| M1046 | 1 | 0.01% |
| M0173 | 1 | 0.01% |
| M0778 | 1 | 0.01% |
| M0658 | 1 | 0.01% |
| M0152 | 1 | 0.01% |
| M0878 | 1 | 0.01% |
| M0843 | 1 | 0.01% |
| M0931 | 1 | 0.01% |
| M0928 | 1 | 0.01% |
| M0550 | 1 | 0.01% |
| M0128 | 1 | 0.01% |
| M0899 | 1 | 0.01% |
| M0555 | 1 | 0.01% |
| M0860 | 1 | 0.01% |
| M0278 | 1 | 0.01% |
| M0531 | 1 | 0.01% |
| M0213 | 1 | 0.01% |
| M0733 | 1 | 0.01% |
| M1072 | 1 | 0.01% |
| M0118 | 1 | 0.01% |
| M0959 | 1 | 0.01% |
| M0440 | 1 | 0.01% |
| M0587 | 1 | 0.01% |
| M0026 | 1 | 0.01% |
| M0883 | 1 | 0.01% |
| M0568 | 1 | 0.01% |
| M0167 | 1 | 0.01% |
| M1103 | 1 | 0.01% |
| M0098 | 1 | 0.01% |
| M1089 | 1 | 0.01% |
| M0570 | 1 | 0.01% |
| M0594 | 1 | 0.01% |
| M0626 | 1 | 0.01% |
| M0056 | 1 | 0.01% |
| M0758 | 1 | 0.01% |
| M0849 | 1 | 0.01% |
| M0869 | 1 | 0.01% |
| M1001 | 1 | 0.01% |
| M1102 | 1 | 0.01% |
| M0597 | 1 | 0.01% |
| M0991 | 1 | 0.01% |
| M0101 | 1 | 0.01% |
| M0736 | 1 | 0.01% |
| M1056 | 1 | 0.01% |
| M0651 | 1 | 0.01% |
| M1042 | 1 | 0.01% |
| M0809 | 1 | 0.01% |
| M0672 | 1 | 0.01% |
| M0621 | 1 | 0.01% |
| M0656 | 1 | 0.01% |
| M0279 | 1 | 0.01% |
| M0032 | 1 | 0.01% |
| M0664 | 1 | 0.01% |
| M1024 | 1 | 0.01% |
| M0387 | 1 | 0.01% |
| M0734 | 1 | 0.01% |
| M1106 | 1 | 0.01% |
| M0122 | 1 | 0.01% |
| M0188 | 1 | 0.01% |
| M0882 | 1 | 0.01% |
| M0866 | 1 | 0.01% |
| M0814 | 1 | 0.01% |
| M0953 | 1 | 0.01% |
| M0210 | 1 | 0.01% |
| M0429 | 1 | 0.01% |
| M0251 | 1 | 0.01% |
| M0035 | 1 | 0.01% |
| M0316 | 1 | 0.01% |
| M0177 | 1 | 0.01% |
| M0987 | 1 | 0.01% |
| M0863 | 1 | 0.01% |
| M0945 | 1 | 0.01% |
| M0769 | 1 | 0.01% |
| M0610 | 1 | 0.01% |
| M0244 | 1 | 0.01% |
| M0907 | 1 | 0.01% |
| M0488 | 1 | 0.01% |
| M0749 | 1 | 0.01% |
| M0865 | 1 | 0.01% |
| M0365 | 1 | 0.01% |
| M0951 | 1 | 0.01% |
| M0634 | 1 | 0.01% |
| M0868 | 1 | 0.01% |
| M0254 | 1 | 0.01% |
| M0526 | 1 | 0.01% |
| M0921 | 1 | 0.01% |
| M0653 | 1 | 0.01% |
| M0235 | 1 | 0.01% |
| M0455 | 1 | 0.01% |
| M0438 | 1 | 0.01% |
| M0355 | 1 | 0.01% |
| M0915 | 1 | 0.01% |
| M0693 | 1 | 0.01% |
| M0785 | 1 | 0.01% |
| M0288 | 1 | 0.01% |
| M0870 | 1 | 0.01% |
| M1076 | 1 | 0.01% |
| M0591 | 1 | 0.01% |
| M0507 | 1 | 0.01% |
| M0496 | 1 | 0.01% |
| M1100 | 1 | 0.01% |
| M0339 | 1 | 0.01% |
| M0532 | 1 | 0.01% |
| M0109 | 1 | 0.01% |
| M0119 | 1 | 0.01% |
| M0036 | 1 | 0.01% |
| M0819 | 1 | 0.01% |
| M1080 | 1 | 0.01% |
| M0824 | 1 | 0.01% |
| M0694 | 1 | 0.01% |
| M0750 | 1 | 0.01% |
| M0960 | 1 | 0.01% |
| M0871 | 1 | 0.01% |
| M1068 | 1 | 0.01% |
| M0554 | 1 | 0.01% |
| M0958 | 1 | 0.01% |
| M0521 | 1 | 0.01% |
| M0499 | 1 | 0.01% |
| M0947 | 1 | 0.01% |
| M0558 | 1 | 0.01% |
| M0226 | 1 | 0.01% |
| M0219 | 1 | 0.01% |
| M0436 | 1 | 0.01% |
| M0582 | 1 | 0.01% |
| M1037 | 1 | 0.01% |
| M0422 | 1 | 0.01% |
| M0248 | 1 | 0.01% |
| M0028 | 1 | 0.01% |
| M1113 | 1 | 0.01% |
| M0027 | 1 | 0.01% |
| M0676 | 1 | 0.01% |
| M0448 | 1 | 0.01% |
| M0596 | 1 | 0.01% |
| M0525 | 1 | 0.01% |
| M0831 | 1 | 0.01% |
| M0569 | 1 | 0.01% |
| M0969 | 1 | 0.01% |
| M0804 | 1 | 0.01% |
| M0901 | 1 | 0.01% |
| M1066 | 1 | 0.01% |
| M0519 | 1 | 0.01% |
| M0483 | 1 | 0.01% |
| M0753 | 1 | 0.01% |
| M0175 | 1 | 0.01% |
| M1081 | 1 | 0.01% |
| M1098 | 1 | 0.01% |
| M0956 | 1 | 0.01% |
| M0576 | 1 | 0.01% |
| M0738 | 1 | 0.01% |
| M0735 | 1 | 0.01% |
| M0796 | 1 | 0.01% |
| M0622 | 1 | 0.01% |
| M0760 | 1 | 0.01% |
| M1047 | 1 | 0.01% |
| M0330 | 1 | 0.01% |
| M1050 | 1 | 0.01% |
| M0759 | 1 | 0.01% |
| M0131 | 1 | 0.01% |
| M0572 | 1 | 0.01% |
| M0646 | 1 | 0.01% |
| M0381 | 1 | 0.01% |
| M0706 | 1 | 0.01% |
| M0540 | 1 | 0.01% |
| M0779 | 1 | 0.01% |
| M0495 | 1 | 0.01% |
| M0767 | 1 | 0.01% |
| M1015 | 1 | 0.01% |
| M0423 | 1 | 0.01% |
| M0522 | 1 | 0.01% |
| M0007 | 1 | 0.01% |
| M0680 | 1 | 0.01% |
| M0888 | 1 | 0.01% |
| M0324 | 1 | 0.01% |
| M0073 | 1 | 0.01% |
| M1036 | 1 | 0.01% |
| M0973 | 1 | 0.01% |
| M1059 | 1 | 0.01% |
| M0338 | 1 | 0.01% |
| M1023 | 1 | 0.01% |
| M1111 | 1 | 0.01% |
| M1043 | 1 | 0.01% |
| M0701 | 1 | 0.01% |
| M0518 | 1 | 0.01% |
| M0584 | 1 | 0.01% |
| M0205 | 1 | 0.01% |
| M0887 | 1 | 0.01% |
| M0946 | 1 | 0.01% |
| M0623 | 1 | 0.01% |
| M1044 | 1 | 0.01% |
| M0082 | 1 | 0.01% |
| M0682 | 1 | 0.01% |
| M1064 | 1 | 0.01% |
| M0068 | 1 | 0.01% |
| M0269 | 1 | 0.01% |
| M0306 | 1 | 0.01% |
| M0212 | 1 | 0.01% |
| M0180 | 1 | 0.01% |
| M0461 | 1 | 0.01% |
| M0726 | 1 | 0.01% |
| M1122 | 1 | 0.01% |
| M0556 | 1 | 0.01% |
| M0727 | 1 | 0.01% |
| M0713 | 1 | 0.01% |
| M0016 | 1 | 0.01% |
| M0349 | 1 | 0.01% |
| M0966 | 1 | 0.01% |
| M1097 | 1 | 0.01% |
| M0270 | 1 | 0.01% |
| M0211 | 1 | 0.01% |
| M0527 | 1 | 0.01% |
| M0886 | 1 | 0.01% |
| M0628 | 1 | 0.01% |
| M1071 | 1 | 0.01% |
| M0162 | 1 | 0.01% |
| M0933 | 1 | 0.01% |
| M0598 | 1 | 0.01% |
| M0433 | 1 | 0.01% |
| M0716 | 1 | 0.01% |
| M1026 | 1 | 0.01% |
| M0856 | 1 | 0.01% |
| M0970 | 1 | 0.01% |
| M0983 | 1 | 0.01% |
| M1055 | 1 | 0.01% |
| M0859 | 1 | 0.01% |
| M0688 | 1 | 0.01% |
| M0089 | 1 | 0.01% |
| M1117 | 1 | 0.01% |
| M0011 | 1 | 0.01% |
| M0506 | 1 | 0.01% |
| M0720 | 1 | 0.01% |
| M1003 | 1 | 0.01% |
| M1061 | 1 | 0.01% |
| M0414 | 1 | 0.01% |
| M0223 | 1 | 0.01% |
| M0194 | 1 | 0.01% |
| M0890 | 1 | 0.01% |
| M0161 | 1 | 0.01% |
| M0662 | 1 | 0.01% |
| M0239 | 1 | 0.01% |
| M0855 | 1 | 0.01% |
| M0303 | 1 | 0.01% |
| M0241 | 1 | 0.01% |
| M0894 | 1 | 0.01% |
| M0225 | 1 | 0.01% |
| M0617 | 1 | 0.01% |
| M0416 | 1 | 0.01% |
| M0842 | 1 | 0.01% |
| M0345 | 1 | 0.01% |
| M0913 | 1 | 0.01% |
| M0781 | 1 | 0.01% |
| M0457 | 1 | 0.01% |
| M0201 | 1 | 0.01% |
| M0552 | 1 | 0.01% |
| M0473 | 1 | 0.01% |
| M0430 | 1 | 0.01% |
| M0060 | 1 | 0.01% |
| M0528 | 1 | 0.01% |
| M0964 | 1 | 0.01% |
| M1075 | 1 | 0.01% |
| M0206 | 1 | 0.01% |
| M1120 | 1 | 0.01% |
| M0977 | 1 | 0.01% |
| M0247 | 1 | 0.01% |
| M1115 | 1 | 0.01% |
| M0995 | 1 | 0.01% |
| M0768 | 1 | 0.01% |
| M0492 | 1 | 0.01% |
| M1092 | 1 | 0.01% |
| M0900 | 1 | 0.01% |
| M0731 | 1 | 0.01% |
| M0704 | 1 | 0.01% |
| M0614 | 1 | 0.01% |
| M0575 | 1 | 0.01% |
| M0942 | 1 | 0.01% |
| M0296 | 1 | 0.01% |
| M0083 | 1 | 0.01% |
| M0553 | 1 | 0.01% |
| M1030 | 1 | 0.01% |
| M1083 | 1 | 0.01% |
| M0512 | 1 | 0.01% |
| M0500 | 1 | 0.01% |
| M0631 | 1 | 0.01% |
| M1118 | 1 | 0.01% |
| M1065 | 1 | 0.01% |
| M0214 | 1 | 0.01% |
| M0459 | 1 | 0.01% |
| M0608 | 1 | 0.01% |
| M0069 | 1 | 0.01% |
| M0412 | 1 | 0.01% |
| M0673 | 1 | 0.01% |
| M1034 | 1 | 0.01% |
| M0485 | 1 | 0.01% |
| M0880 | 1 | 0.01% |
| M0227 | 1 | 0.01% |
| M0846 | 1 | 0.01% |
| M0916 | 1 | 0.01% |
| M0373 | 1 | 0.01% |
| M0322 | 1 | 0.01% |
| M0465 | 1 | 0.01% |
| M0721 | 1 | 0.01% |
| M1009 | 1 | 0.01% |
| M0407 | 1 | 0.01% |
| M0640 | 1 | 0.01% |
| M0426 | 1 | 0.01% |
| M0577 | 1 | 0.01% |
| M0103 | 1 | 0.01% |
| M0962 | 1 | 0.01% |
| M0113 | 1 | 0.01% |
| M0334 | 1 | 0.01% |
| M0342 | 1 | 0.01% |
| M0401 | 1 | 0.01% |
| M0746 | 1 | 0.01% |
| M1018 | 1 | 0.01% |
| M1107 | 1 | 0.01% |
| M1053 | 1 | 0.01% |
| M0976 | 1 | 0.01% |
| M1038 | 1 | 0.01% |
| M0879 | 1 | 0.01% |
| M0834 | 1 | 0.01% |
| M1014 | 1 | 0.01% |
| M0562 | 1 | 0.01% |
| M0873 | 1 | 0.01% |
| M0092 | 1 | 0.01% |
| M0224 | 1 | 0.01% |
| M0994 | 1 | 0.01% |
| M0952 | 1 | 0.01% |
| M0712 | 1 | 0.01% |
| M1067 | 1 | 0.01% |
| M0599 | 1 | 0.01% |
| M1078 | 1 | 0.01% |
| M0329 | 1 | 0.01% |
| M1121 | 1 | 0.01% |
| M0691 | 1 | 0.01% |
| M0692 | 1 | 0.01% |
| M0047 | 1 | 0.01% |
| M0335 | 1 | 0.01% |
| M0979 | 1 | 0.01% |
| M0165 | 1 | 0.01% |
| M0625 | 1 | 0.01% |
| M0910 | 1 | 0.01% |
| M0602 | 1 | 0.01% |
| M0157 | 1 | 0.01% |
| M0331 | 1 | 0.01% |
| M0655 | 1 | 0.01% |
| M0097 | 1 | 0.01% |
| M0289 | 1 | 0.01% |
| M0408 | 1 | 0.01% |
| M0601 | 1 | 0.01% |
| M0667 | 1 | 0.01% |
| M0806 | 1 | 0.01% |
| M0854 | 1 | 0.01% |
| M0675 | 1 | 0.01% |
| M0730 | 1 | 0.01% |
| M0044 | 1 | 0.01% |
| M0605 | 1 | 0.01% |
| M0766 | 1 | 0.01% |
| M0698 | 1 | 0.01% |
| M0108 | 1 | 0.01% |
| M0548 | 1 | 0.01% |
| M0065 | 1 | 0.01% |
| M0561 | 1 | 0.01% |
| M1057 | 1 | 0.01% |
| M0711 | 1 | 0.01% |
| M0347 | 1 | 0.01% |
| M0078 | 1 | 0.01% |
| M0965 | 1 | 0.01% |
| M0286 | 1 | 0.01% |
| M0844 | 1 | 0.01% |
| M0586 | 1 | 0.01% |
| M1091 | 1 | 0.01% |
| M1021 | 1 | 0.01% |
| M0385 | 1 | 0.01% |
| M0782 | 1 | 0.01% |
| M0535 | 1 | 0.01% |
| M0932 | 1 | 0.01% |
| M0311 | 1 | 0.01% |
| M1051 | 1 | 0.01% |
| M0784 | 1 | 0.01% |
| M0957 | 1 | 0.01% |
| M0015 | 1 | 0.01% |
| M0516 | 1 | 0.01% |
| M1006 | 1 | 0.01% |
| M1002 | 1 | 0.01% |
| M1054 | 1 | 0.01% |
| M0432 | 1 | 0.01% |
| M1112 | 1 | 0.01% |
| M0661 | 1 | 0.01% |
| M0803 | 1 | 0.01% |
| M0293 | 1 | 0.01% |
| M0163 | 1 | 0.01% |
| M0668 | 1 | 0.01% |
| M0186 | 1 | 0.01% |
| M1088 | 1 | 0.01% |
| M0909 | 1 | 0.01% |
| M0337 | 1 | 0.01% |
| M0232 | 1 | 0.01% |
| M0937 | 1 | 0.01% |
| M0395 | 1 | 0.01% |
| M0867 | 1 | 0.01% |
| M0470 | 1 | 0.01% |
| M0950 | 1 | 0.01% |
| M0321 | 1 | 0.01% |
| M0369 | 1 | 0.01% |
| M0637 | 1 | 0.01% |
| M0265 | 1 | 0.01% |
| M0253 | 1 | 0.01% |
| M0771 | 1 | 0.01% |
| M1116 | 1 | 0.01% |
| M0425 | 1 | 0.01% |
| M0902 | 1 | 0.01% |
| M0055 | 1 | 0.01% |
| M0318 | 1 | 0.01% |
| M0340 | 1 | 0.01% |
| M0377 | 1 | 0.01% |
| M0884 | 1 | 0.01% |
| M0709 | 1 | 0.01% |
| M0574 | 1 | 0.01% |
| M0840 | 1 | 0.01% |
| M0061 | 1 | 0.01% |
data['Merchant_ID'].replace({'M0001':1}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-129 | 1 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-073 | 1 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-129 | 1 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
unused = data['Merchant_ID'].loc[data['Merchant_ID']!= 1]
data.replace(unused.values, 0, inplace=True)
data
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-129 | 1 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-073 | 1 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-129 | 1 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | 2018-12-31 15:47:34.782 | 0 | 5000000.0 | 1 | 1 | 1 | 1 | CTY06-023 | 1 | OEDC0377 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | 2018-12-31 15:47:34.782 | 0 | 4800000.0 | 0 | 1 | 1 | 1 | CTY06-181 | 1 | OEDC0377 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | 2018-12-31 15:47:34.782 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | CTY06-023 | 1 | OEDC0377 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | 2018-12-31 15:47:34.782 | 0 | 1100000.0 | 0 | 1 | 1 | 1 | CTY06-186 | 1 | OEDC0377 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | 2018-12-31 22:11:05.961 | 0 | 102500.0 | 0 | 1 | 0 | 0 | CTY06-171 | 1 | OEDC0633 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10500 rows × 15 columns
value_counts(data, 'City_ID')
| Value | Count | Percentage |
|---|---|---|
| CTY06-023 | 4,255 | 40.52% |
| CTY06-133 | 1,277 | 12.16% |
| CTY06-181 | 801 | 7.63% |
| CTY06-171 | 441 | 4.20% |
| CTY06-186 | 337 | 3.21% |
| CTY06-129 | 263 | 2.50% |
| CTY06-005 | 173 | 1.65% |
| CTY06-112 | 156 | 1.49% |
| CTY06-004 | 145 | 1.38% |
| CTY06-151 | 113 | 1.08% |
| CTY06-083 | 103 | 0.98% |
| CTY06-110 | 101 | 0.96% |
| CTY06-191 | 94 | 0.90% |
| CTY06-205 | 84 | 0.80% |
| CTY06-115 | 77 | 0.73% |
| CTY06-179 | 75 | 0.71% |
| CTY06-075 | 73 | 0.70% |
| CTY06-126 | 68 | 0.65% |
| CTY06-046 | 65 | 0.62% |
| CTY06-140 | 64 | 0.61% |
| CTY06-101 | 62 | 0.59% |
| CTY06-176 | 60 | 0.57% |
| CTY06-121 | 59 | 0.56% |
| CTY06-195 | 59 | 0.56% |
| CTY06-072 | 58 | 0.55% |
| CTY06-058 | 55 | 0.52% |
| CTY06-096 | 45 | 0.43% |
| CTY06-071 | 43 | 0.41% |
| CTY06-027 | 43 | 0.41% |
| CTY06-073 | 41 | 0.39% |
| CTY06-031 | 39 | 0.37% |
| CTY06-099 | 39 | 0.37% |
| CTY06-029 | 38 | 0.36% |
| CTY06-013 | 37 | 0.35% |
| CTY06-173 | 35 | 0.33% |
| CTY06-035 | 35 | 0.33% |
| CTY06-090 | 35 | 0.33% |
| CTY06-049 | 30 | 0.29% |
| CTY06-067 | 29 | 0.28% |
| CTY06-169 | 29 | 0.28% |
| CTY06-087 | 27 | 0.26% |
| CTY06-168 | 27 | 0.26% |
| CTY06-197 | 27 | 0.26% |
| CTY06-056 | 24 | 0.23% |
| CTY06-109 | 23 | 0.22% |
| CTY06-167 | 23 | 0.22% |
| CTY06-142 | 21 | 0.20% |
| CTY06-202 | 20 | 0.19% |
| CTY06-125 | 20 | 0.19% |
| CTY06-015 | 19 | 0.18% |
| CTY06-183 | 19 | 0.18% |
| CTY06-077 | 19 | 0.18% |
| CTY06-059 | 17 | 0.16% |
| CTY06-089 | 17 | 0.16% |
| CTY06-200 | 16 | 0.15% |
| CTY06-033 | 16 | 0.15% |
| CTY06-164 | 16 | 0.15% |
| CTY06-170 | 15 | 0.14% |
| CTY06-068 | 15 | 0.14% |
| CTY06-119 | 14 | 0.13% |
| CTY06-086 | 14 | 0.13% |
| CTY06-147 | 13 | 0.12% |
| CTY06-036 | 12 | 0.11% |
| CTY06-128 | 12 | 0.11% |
| CTY06-095 | 12 | 0.11% |
| CTY06-193 | 11 | 0.10% |
| CTY06-185 | 10 | 0.10% |
| CTY06-080 | 10 | 0.10% |
| CTY06-113 | 9 | 0.09% |
| CTY06-180 | 9 | 0.09% |
| CTY06-001 | 8 | 0.08% |
| CTY06-139 | 8 | 0.08% |
| CTY06-162 | 8 | 0.08% |
| CTY06-030 | 8 | 0.08% |
| CTY06-120 | 7 | 0.07% |
| CTY06-098 | 7 | 0.07% |
| CTY06-010 | 7 | 0.07% |
| CTY06-166 | 7 | 0.07% |
| CTY06-044 | 7 | 0.07% |
| CTY06-040 | 7 | 0.07% |
| CTY06-174 | 6 | 0.06% |
| CTY06-159 | 6 | 0.06% |
| CTY06-105 | 6 | 0.06% |
| CTY06-134 | 6 | 0.06% |
| CTY06-085 | 5 | 0.05% |
| CTY06-194 | 5 | 0.05% |
| CTY06-155 | 5 | 0.05% |
| CTY06-145 | 5 | 0.05% |
| CTY06-063 | 5 | 0.05% |
| CTY06-025 | 4 | 0.04% |
| CTY06-057 | 4 | 0.04% |
| CTY06-037 | 4 | 0.04% |
| CTY06-060 | 4 | 0.04% |
| CTY06-074 | 4 | 0.04% |
| CTY06-158 | 4 | 0.04% |
| CTY06-149 | 4 | 0.04% |
| CTY06-048 | 4 | 0.04% |
| CTY06-175 | 4 | 0.04% |
| CTY06-146 | 4 | 0.04% |
| CTY06-002 | 4 | 0.04% |
| CTY06-022 | 4 | 0.04% |
| CTY06-064 | 4 | 0.04% |
| CTY06-051 | 3 | 0.03% |
| CTY06-154 | 3 | 0.03% |
| CTY06-192 | 3 | 0.03% |
| CTY06-152 | 3 | 0.03% |
| CTY06-117 | 3 | 0.03% |
| CTY06-034 | 3 | 0.03% |
| CTY07-002 | 3 | 0.03% |
| CTY06-093 | 3 | 0.03% |
| CTY06-039 | 3 | 0.03% |
| CTY06-123 | 3 | 0.03% |
| CTY06-009 | 3 | 0.03% |
| CTY06-021 | 3 | 0.03% |
| CTY06-104 | 3 | 0.03% |
| CTY06-079 | 2 | 0.02% |
| CTY06-062 | 2 | 0.02% |
| CTY05-005 | 2 | 0.02% |
| CTY06-189 | 2 | 0.02% |
| CTY06-061 | 2 | 0.02% |
| CTY06-102 | 2 | 0.02% |
| CTY06-003 | 2 | 0.02% |
| CTY06-172 | 2 | 0.02% |
| CTY06-108 | 2 | 0.02% |
| CTY06-182 | 2 | 0.02% |
| CTY06-082 | 2 | 0.02% |
| CTY06-028 | 2 | 0.02% |
| CTY06-006 | 2 | 0.02% |
| CTY06-160 | 2 | 0.02% |
| CTY06-018 | 2 | 0.02% |
| CTY08-002 | 2 | 0.02% |
| CTY06-203 | 2 | 0.02% |
| CTY06-012 | 2 | 0.02% |
| CTY05-004 | 2 | 0.02% |
| CTY06-017 | 2 | 0.02% |
| CTY06-020 | 2 | 0.02% |
| CTY06-131 | 2 | 0.02% |
| CTY06-007 | 2 | 0.02% |
| CTY06-153 | 2 | 0.02% |
| CTY06-094 | 2 | 0.02% |
| CTY06-103 | 2 | 0.02% |
| CTY06-038 | 2 | 0.02% |
| CTY06-092 | 2 | 0.02% |
| CTY06-190 | 2 | 0.02% |
| CTY06-111 | 2 | 0.02% |
| CTY06-042 | 2 | 0.02% |
| CTY13-002 | 1 | 0.01% |
| CTY06-008 | 1 | 0.01% |
| CTY08-006 | 1 | 0.01% |
| CTY06-019 | 1 | 0.01% |
| CTY06-114 | 1 | 0.01% |
| CTY06-116 | 1 | 0.01% |
| CTY10-001 | 1 | 0.01% |
| CTY06-011 | 1 | 0.01% |
| CTY07-003 | 1 | 0.01% |
| CTY13-003 | 1 | 0.01% |
| CTY09-002 | 1 | 0.01% |
| CTY06-137 | 1 | 0.01% |
| CTY06-148 | 1 | 0.01% |
| CTY06-100 | 1 | 0.01% |
| CTY06-014 | 1 | 0.01% |
| CTY05-003 | 1 | 0.01% |
| CTY06-201 | 1 | 0.01% |
| CTY02-001 | 1 | 0.01% |
| CTY12-002 | 1 | 0.01% |
| CTY06-052 | 1 | 0.01% |
| CTY08-004 | 1 | 0.01% |
| CTY06-078 | 1 | 0.01% |
| CTY06-076 | 1 | 0.01% |
| CTY06-097 | 1 | 0.01% |
| CTY06-204 | 1 | 0.01% |
| CTY01-002 | 1 | 0.01% |
| CTY05-002 | 1 | 0.01% |
| CTY10-002 | 1 | 0.01% |
| CTY06-130 | 1 | 0.01% |
| CTY06-065 | 1 | 0.01% |
| CTY06-127 | 1 | 0.01% |
| CTY06-165 | 1 | 0.01% |
| CTY06-199 | 1 | 0.01% |
| CTY08-005 | 1 | 0.01% |
| CTY06-150 | 1 | 0.01% |
| CTY06-163 | 1 | 0.01% |
| CTY06-144 | 1 | 0.01% |
| CTY04-001 | 1 | 0.01% |
| CTY06-084 | 1 | 0.01% |
| CTY06-088 | 1 | 0.01% |
| CTY08-007 | 1 | 0.01% |
| CTY06-047 | 1 | 0.01% |
| CTY06-177 | 1 | 0.01% |
| CTY06-122 | 1 | 0.01% |
| CTY06-069 | 1 | 0.01% |
| CTY06-043 | 1 | 0.01% |
| CTY07-001 | 1 | 0.01% |
| CTY12-001 | 1 | 0.01% |
| CTY08-003 | 1 | 0.01% |
| CTY01-001 | 1 | 0.01% |
| CTY06-106 | 1 | 0.01% |
| CTY08-001 | 1 | 0.01% |
| CTY06-124 | 1 | 0.01% |
| CTY05-001 | 1 | 0.01% |
| CTY06-050 | 1 | 0.01% |
| CTY06-143 | 1 | 0.01% |
| CTY06-026 | 1 | 0.01% |
| CTY01-003 | 1 | 0.01% |
| CTY06-053 | 1 | 0.01% |
| CTY05-007 | 1 | 0.01% |
| CTY06-136 | 1 | 0.01% |
| CTY06-188 | 1 | 0.01% |
| CTY06-157 | 1 | 0.01% |
| CTY06-198 | 1 | 0.01% |
| CTY06-024 | 1 | 0.01% |
| CTY06-141 | 1 | 0.01% |
| CTY06-161 | 1 | 0.01% |
| CTY13-001 | 1 | 0.01% |
| CTY06-178 | 1 | 0.01% |
| CTY06-138 | 1 | 0.01% |
| CTY06-132 | 1 | 0.01% |
| CTY06-107 | 1 | 0.01% |
| CTY06-135 | 1 | 0.01% |
| CTY08-008 | 1 | 0.01% |
| CTY09-001 | 1 | 0.01% |
| CTY06-187 | 1 | 0.01% |
| CTY06-184 | 1 | 0.01% |
| CTY06-041 | 1 | 0.01% |
| CTY06-091 | 1 | 0.01% |
| CTY06-054 | 1 | 0.01% |
| CTY06-118 | 1 | 0.01% |
data['City_ID'].replace({'CTY06-023':0}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-129 | 1 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-073 | 1 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | CTY06-129 | 1 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
unused = data['City_ID'].loc[data['City_ID']!= 0]
data.replace(unused.values, 1, inplace=True)
data
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | 2018-12-31 15:47:34.782 | 0 | 5000000.0 | 1 | 1 | 1 | 1 | 0 | 1 | OEDC0377 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | 2018-12-31 15:47:34.782 | 0 | 4800000.0 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | 2018-12-31 15:47:34.782 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 0 | 1 | OEDC0377 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | 2018-12-31 15:47:34.782 | 0 | 1100000.0 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | 2018-12-31 22:11:05.961 | 0 | 102500.0 | 0 | 1 | 0 | 0 | 1 | 1 | OEDC0633 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10500 rows × 15 columns
value_counts(data, 'EDC_Owner')
| Value | Count | Percentage |
|---|---|---|
| OEDC0377 | 8,325 | 79.29% |
| OEDC0378 | 15 | 0.14% |
| OEDC0212 | 6 | 0.06% |
| OEDC1363 | 6 | 0.06% |
| OEDC0886 | 6 | 0.06% |
| OEDC0343 | 5 | 0.05% |
| OEDC1639 | 5 | 0.05% |
| OEDC1444 | 5 | 0.05% |
| OEDC1261 | 5 | 0.05% |
| OEDC1048 | 5 | 0.05% |
| OEDC0909 | 5 | 0.05% |
| OEDC0380 | 5 | 0.05% |
| OEDC1035 | 5 | 0.05% |
| OEDC1438 | 5 | 0.05% |
| OEDC0180 | 5 | 0.05% |
| OEDC1085 | 5 | 0.05% |
| OEDC1288 | 5 | 0.05% |
| OEDC0118 | 5 | 0.05% |
| OEDC1040 | 5 | 0.05% |
| OEDC1539 | 5 | 0.05% |
| OEDC0113 | 4 | 0.04% |
| OEDC0101 | 4 | 0.04% |
| OEDC0449 | 4 | 0.04% |
| OEDC1066 | 4 | 0.04% |
| OEDC0059 | 4 | 0.04% |
| OEDC0931 | 4 | 0.04% |
| OEDC0291 | 4 | 0.04% |
| OEDC0071 | 4 | 0.04% |
| OEDC1520 | 4 | 0.04% |
| OEDC1327 | 4 | 0.04% |
| OEDC1285 | 4 | 0.04% |
| OEDC0751 | 4 | 0.04% |
| OEDC0829 | 4 | 0.04% |
| OEDC1159 | 4 | 0.04% |
| OEDC1430 | 4 | 0.04% |
| OEDC0890 | 4 | 0.04% |
| OEDC1523 | 4 | 0.04% |
| OEDC0089 | 4 | 0.04% |
| OEDC1107 | 4 | 0.04% |
| OEDC0914 | 4 | 0.04% |
| OEDC0052 | 4 | 0.04% |
| OEDC0392 | 4 | 0.04% |
| OEDC1467 | 4 | 0.04% |
| OEDC1452 | 4 | 0.04% |
| OEDC1573 | 4 | 0.04% |
| OEDC0441 | 4 | 0.04% |
| OEDC1375 | 4 | 0.04% |
| OEDC1528 | 3 | 0.03% |
| OEDC0873 | 3 | 0.03% |
| OEDC0091 | 3 | 0.03% |
| OEDC0308 | 3 | 0.03% |
| OEDC0004 | 3 | 0.03% |
| OEDC0878 | 3 | 0.03% |
| OEDC1097 | 3 | 0.03% |
| OEDC1350 | 3 | 0.03% |
| OEDC0178 | 3 | 0.03% |
| OEDC1021 | 3 | 0.03% |
| OEDC1636 | 3 | 0.03% |
| OEDC1228 | 3 | 0.03% |
| OEDC0845 | 3 | 0.03% |
| OEDC0944 | 3 | 0.03% |
| OEDC0275 | 3 | 0.03% |
| OEDC0908 | 3 | 0.03% |
| OEDC0823 | 3 | 0.03% |
| OEDC0106 | 3 | 0.03% |
| OEDC0236 | 3 | 0.03% |
| OEDC0792 | 3 | 0.03% |
| OEDC0735 | 3 | 0.03% |
| OEDC0470 | 3 | 0.03% |
| OEDC1642 | 3 | 0.03% |
| OEDC1442 | 3 | 0.03% |
| OEDC1045 | 3 | 0.03% |
| OEDC0230 | 3 | 0.03% |
| OEDC0578 | 3 | 0.03% |
| OEDC1601 | 3 | 0.03% |
| OEDC0025 | 3 | 0.03% |
| OEDC0363 | 3 | 0.03% |
| OEDC0350 | 3 | 0.03% |
| OEDC1211 | 3 | 0.03% |
| OEDC0477 | 3 | 0.03% |
| OEDC1395 | 3 | 0.03% |
| OEDC1493 | 3 | 0.03% |
| OEDC1421 | 3 | 0.03% |
| OEDC0443 | 3 | 0.03% |
| OEDC0811 | 3 | 0.03% |
| OEDC0293 | 3 | 0.03% |
| OEDC0082 | 3 | 0.03% |
| OEDC1082 | 3 | 0.03% |
| OEDC0870 | 3 | 0.03% |
| OEDC0965 | 3 | 0.03% |
| OEDC1223 | 3 | 0.03% |
| OEDC0953 | 3 | 0.03% |
| OEDC1586 | 3 | 0.03% |
| OEDC0310 | 3 | 0.03% |
| OEDC0648 | 3 | 0.03% |
| OEDC1295 | 3 | 0.03% |
| OEDC0084 | 3 | 0.03% |
| OEDC0047 | 3 | 0.03% |
| OEDC0654 | 3 | 0.03% |
| OEDC1186 | 3 | 0.03% |
| OEDC1129 | 3 | 0.03% |
| OEDC0712 | 3 | 0.03% |
| OEDC1634 | 3 | 0.03% |
| OEDC1641 | 3 | 0.03% |
| OEDC1077 | 3 | 0.03% |
| OEDC0598 | 3 | 0.03% |
| OEDC0121 | 3 | 0.03% |
| OEDC1234 | 3 | 0.03% |
| OEDC0183 | 3 | 0.03% |
| OEDC0192 | 3 | 0.03% |
| OEDC0046 | 3 | 0.03% |
| OEDC1270 | 3 | 0.03% |
| OEDC0325 | 3 | 0.03% |
| OEDC0761 | 3 | 0.03% |
| OEDC0817 | 3 | 0.03% |
| OEDC1200 | 3 | 0.03% |
| OEDC1582 | 3 | 0.03% |
| OEDC0465 | 3 | 0.03% |
| OEDC1322 | 3 | 0.03% |
| OEDC1411 | 3 | 0.03% |
| OEDC0722 | 3 | 0.03% |
| OEDC0501 | 3 | 0.03% |
| OEDC0493 | 3 | 0.03% |
| OEDC0251 | 3 | 0.03% |
| OEDC0124 | 3 | 0.03% |
| OEDC1051 | 3 | 0.03% |
| OEDC0881 | 3 | 0.03% |
| OEDC0901 | 3 | 0.03% |
| OEDC1148 | 3 | 0.03% |
| OEDC1317 | 3 | 0.03% |
| OEDC0345 | 3 | 0.03% |
| OEDC0570 | 3 | 0.03% |
| OEDC1100 | 3 | 0.03% |
| OEDC1293 | 3 | 0.03% |
| OEDC1605 | 3 | 0.03% |
| OEDC1414 | 3 | 0.03% |
| OEDC1404 | 3 | 0.03% |
| OEDC0151 | 3 | 0.03% |
| OEDC1436 | 3 | 0.03% |
| OEDC0747 | 3 | 0.03% |
| OEDC0885 | 2 | 0.02% |
| OEDC0671 | 2 | 0.02% |
| OEDC1369 | 2 | 0.02% |
| OEDC0848 | 2 | 0.02% |
| OEDC1006 | 2 | 0.02% |
| OEDC1370 | 2 | 0.02% |
| OEDC1053 | 2 | 0.02% |
| OEDC0907 | 2 | 0.02% |
| OEDC1188 | 2 | 0.02% |
| OEDC1215 | 2 | 0.02% |
| OEDC0467 | 2 | 0.02% |
| OEDC1649 | 2 | 0.02% |
| OEDC1624 | 2 | 0.02% |
| OEDC1629 | 2 | 0.02% |
| OEDC0656 | 2 | 0.02% |
| OEDC0360 | 2 | 0.02% |
| OEDC1123 | 2 | 0.02% |
| OEDC0815 | 2 | 0.02% |
| OEDC1240 | 2 | 0.02% |
| OEDC1191 | 2 | 0.02% |
| OEDC1224 | 2 | 0.02% |
| OEDC0830 | 2 | 0.02% |
| OEDC0846 | 2 | 0.02% |
| OEDC1417 | 2 | 0.02% |
| OEDC1519 | 2 | 0.02% |
| OEDC0138 | 2 | 0.02% |
| OEDC0199 | 2 | 0.02% |
| OEDC0618 | 2 | 0.02% |
| OEDC0188 | 2 | 0.02% |
| OEDC1180 | 2 | 0.02% |
| OEDC0796 | 2 | 0.02% |
| OEDC0962 | 2 | 0.02% |
| OEDC1562 | 2 | 0.02% |
| OEDC1163 | 2 | 0.02% |
| OEDC1072 | 2 | 0.02% |
| OEDC1431 | 2 | 0.02% |
| OEDC0026 | 2 | 0.02% |
| OEDC0778 | 2 | 0.02% |
| OEDC0562 | 2 | 0.02% |
| OEDC0676 | 2 | 0.02% |
| OEDC0279 | 2 | 0.02% |
| OEDC1050 | 2 | 0.02% |
| OEDC1216 | 2 | 0.02% |
| OEDC0083 | 2 | 0.02% |
| OEDC1475 | 2 | 0.02% |
| OEDC1020 | 2 | 0.02% |
| OEDC1473 | 2 | 0.02% |
| OEDC0103 | 2 | 0.02% |
| OEDC1056 | 2 | 0.02% |
| OEDC0434 | 2 | 0.02% |
| OEDC1347 | 2 | 0.02% |
| OEDC1613 | 2 | 0.02% |
| OEDC1348 | 2 | 0.02% |
| OEDC0349 | 2 | 0.02% |
| OEDC1131 | 2 | 0.02% |
| OEDC0391 | 2 | 0.02% |
| OEDC0683 | 2 | 0.02% |
| OEDC0930 | 2 | 0.02% |
| OEDC0952 | 2 | 0.02% |
| OEDC1644 | 2 | 0.02% |
| OEDC0137 | 2 | 0.02% |
| OEDC0866 | 2 | 0.02% |
| OEDC0993 | 2 | 0.02% |
| OEDC0785 | 2 | 0.02% |
| OEDC0651 | 2 | 0.02% |
| OEDC0672 | 2 | 0.02% |
| OEDC0481 | 2 | 0.02% |
| OEDC0437 | 2 | 0.02% |
| OEDC0418 | 2 | 0.02% |
| OEDC1259 | 2 | 0.02% |
| OEDC1480 | 2 | 0.02% |
| OEDC1453 | 2 | 0.02% |
| OEDC1401 | 2 | 0.02% |
| OEDC0859 | 2 | 0.02% |
| OEDC0359 | 2 | 0.02% |
| OEDC1249 | 2 | 0.02% |
| OEDC0450 | 2 | 0.02% |
| OEDC1251 | 2 | 0.02% |
| OEDC1305 | 2 | 0.02% |
| OEDC0955 | 2 | 0.02% |
| OEDC1487 | 2 | 0.02% |
| OEDC0585 | 2 | 0.02% |
| OEDC1429 | 2 | 0.02% |
| OEDC0945 | 2 | 0.02% |
| OEDC0992 | 2 | 0.02% |
| OEDC0242 | 2 | 0.02% |
| OEDC1306 | 2 | 0.02% |
| OEDC1175 | 2 | 0.02% |
| OEDC0625 | 2 | 0.02% |
| OEDC1063 | 2 | 0.02% |
| OEDC1457 | 2 | 0.02% |
| OEDC1166 | 2 | 0.02% |
| OEDC1276 | 2 | 0.02% |
| OEDC0258 | 2 | 0.02% |
| OEDC0174 | 2 | 0.02% |
| OEDC0772 | 2 | 0.02% |
| OEDC0069 | 2 | 0.02% |
| OEDC1016 | 2 | 0.02% |
| OEDC0086 | 2 | 0.02% |
| OEDC1508 | 2 | 0.02% |
| OEDC1014 | 2 | 0.02% |
| OEDC0652 | 2 | 0.02% |
| OEDC1144 | 2 | 0.02% |
| OEDC1335 | 2 | 0.02% |
| OEDC0573 | 2 | 0.02% |
| OEDC0682 | 2 | 0.02% |
| OEDC1019 | 2 | 0.02% |
| OEDC0579 | 2 | 0.02% |
| OEDC0335 | 2 | 0.02% |
| OEDC1263 | 2 | 0.02% |
| OEDC1220 | 2 | 0.02% |
| OEDC1145 | 2 | 0.02% |
| OEDC0181 | 2 | 0.02% |
| OEDC0659 | 2 | 0.02% |
| OEDC0553 | 2 | 0.02% |
| OEDC0540 | 2 | 0.02% |
| OEDC1434 | 2 | 0.02% |
| OEDC1146 | 2 | 0.02% |
| OEDC1338 | 2 | 0.02% |
| OEDC0593 | 2 | 0.02% |
| OEDC0334 | 2 | 0.02% |
| OEDC0951 | 2 | 0.02% |
| OEDC1616 | 2 | 0.02% |
| OEDC0336 | 2 | 0.02% |
| OEDC0850 | 2 | 0.02% |
| OEDC0479 | 2 | 0.02% |
| OEDC0131 | 2 | 0.02% |
| OEDC0763 | 2 | 0.02% |
| OEDC1583 | 2 | 0.02% |
| OEDC0957 | 2 | 0.02% |
| OEDC1522 | 2 | 0.02% |
| OEDC0820 | 2 | 0.02% |
| OEDC0256 | 2 | 0.02% |
| OEDC0216 | 2 | 0.02% |
| OEDC0364 | 2 | 0.02% |
| OEDC1300 | 2 | 0.02% |
| OEDC0210 | 2 | 0.02% |
| OEDC0133 | 2 | 0.02% |
| OEDC1459 | 2 | 0.02% |
| OEDC0887 | 2 | 0.02% |
| OEDC0432 | 2 | 0.02% |
| OEDC0252 | 2 | 0.02% |
| OEDC0948 | 2 | 0.02% |
| OEDC0110 | 2 | 0.02% |
| OEDC0431 | 2 | 0.02% |
| OEDC0043 | 2 | 0.02% |
| OEDC1530 | 2 | 0.02% |
| OEDC0655 | 2 | 0.02% |
| OEDC1494 | 2 | 0.02% |
| OEDC0565 | 2 | 0.02% |
| OEDC0996 | 2 | 0.02% |
| OEDC1384 | 2 | 0.02% |
| OEDC0690 | 2 | 0.02% |
| OEDC0476 | 2 | 0.02% |
| OEDC0460 | 2 | 0.02% |
| OEDC0141 | 2 | 0.02% |
| OEDC1618 | 2 | 0.02% |
| OEDC1626 | 2 | 0.02% |
| OEDC0614 | 2 | 0.02% |
| OEDC0753 | 2 | 0.02% |
| OEDC0911 | 2 | 0.02% |
| OEDC0882 | 2 | 0.02% |
| OEDC0356 | 2 | 0.02% |
| OEDC0875 | 2 | 0.02% |
| OEDC1482 | 2 | 0.02% |
| OEDC0596 | 2 | 0.02% |
| OEDC1081 | 2 | 0.02% |
| OEDC0918 | 2 | 0.02% |
| OEDC0167 | 2 | 0.02% |
| OEDC0463 | 2 | 0.02% |
| OEDC0688 | 2 | 0.02% |
| OEDC0320 | 2 | 0.02% |
| OEDC1597 | 2 | 0.02% |
| OEDC1598 | 2 | 0.02% |
| OEDC0042 | 2 | 0.02% |
| OEDC1170 | 2 | 0.02% |
| OEDC0800 | 2 | 0.02% |
| OEDC1526 | 2 | 0.02% |
| OEDC0029 | 2 | 0.02% |
| OEDC0633 | 2 | 0.02% |
| OEDC0039 | 2 | 0.02% |
| OEDC0641 | 2 | 0.02% |
| OEDC0822 | 2 | 0.02% |
| OEDC0925 | 2 | 0.02% |
| OEDC0157 | 2 | 0.02% |
| OEDC0828 | 2 | 0.02% |
| OEDC1193 | 2 | 0.02% |
| OEDC1342 | 2 | 0.02% |
| OEDC0011 | 2 | 0.02% |
| OEDC0542 | 2 | 0.02% |
| OEDC1094 | 2 | 0.02% |
| OEDC1665 | 2 | 0.02% |
| OEDC0196 | 2 | 0.02% |
| OEDC0841 | 2 | 0.02% |
| OEDC0051 | 2 | 0.02% |
| OEDC1483 | 2 | 0.02% |
| OEDC1439 | 2 | 0.02% |
| OEDC0062 | 2 | 0.02% |
| OEDC1239 | 2 | 0.02% |
| OEDC1319 | 2 | 0.02% |
| OEDC1139 | 2 | 0.02% |
| OEDC0309 | 2 | 0.02% |
| OEDC1399 | 2 | 0.02% |
| OEDC1162 | 2 | 0.02% |
| OEDC0826 | 2 | 0.02% |
| OEDC1572 | 2 | 0.02% |
| OEDC0109 | 2 | 0.02% |
| OEDC0947 | 2 | 0.02% |
| OEDC0773 | 2 | 0.02% |
| OEDC0097 | 2 | 0.02% |
| OEDC0126 | 2 | 0.02% |
| OEDC1485 | 2 | 0.02% |
| OEDC0447 | 2 | 0.02% |
| OEDC1419 | 2 | 0.02% |
| OEDC0154 | 2 | 0.02% |
| OEDC0621 | 2 | 0.02% |
| OEDC0867 | 2 | 0.02% |
| OEDC0715 | 2 | 0.02% |
| OEDC0427 | 2 | 0.02% |
| OEDC0306 | 2 | 0.02% |
| OEDC1181 | 2 | 0.02% |
| OEDC0401 | 2 | 0.02% |
| OEDC0705 | 2 | 0.02% |
| OEDC0966 | 2 | 0.02% |
| OEDC0568 | 2 | 0.02% |
| OEDC1225 | 2 | 0.02% |
| OEDC1247 | 2 | 0.02% |
| OEDC1108 | 2 | 0.02% |
| OEDC0587 | 2 | 0.02% |
| OEDC1575 | 2 | 0.02% |
| OEDC1659 | 2 | 0.02% |
| OEDC1283 | 2 | 0.02% |
| OEDC1196 | 2 | 0.02% |
| OEDC1581 | 2 | 0.02% |
| OEDC1619 | 2 | 0.02% |
| OEDC1652 | 2 | 0.02% |
| OEDC1420 | 2 | 0.02% |
| OEDC0687 | 2 | 0.02% |
| OEDC0281 | 2 | 0.02% |
| OEDC1214 | 2 | 0.02% |
| OEDC0888 | 2 | 0.02% |
| OEDC0255 | 2 | 0.02% |
| OEDC0917 | 2 | 0.02% |
| OEDC0143 | 2 | 0.02% |
| OEDC0273 | 2 | 0.02% |
| OEDC1099 | 2 | 0.02% |
| OEDC0693 | 2 | 0.02% |
| OEDC0257 | 2 | 0.02% |
| OEDC0466 | 2 | 0.02% |
| OEDC1578 | 2 | 0.02% |
| OEDC0290 | 2 | 0.02% |
| OEDC0525 | 2 | 0.02% |
| OEDC0741 | 2 | 0.02% |
| OEDC0880 | 2 | 0.02% |
| OEDC1127 | 2 | 0.02% |
| OEDC0764 | 2 | 0.02% |
| OEDC0838 | 2 | 0.02% |
| OEDC0056 | 2 | 0.02% |
| OEDC0142 | 2 | 0.02% |
| OEDC0329 | 2 | 0.02% |
| OEDC1134 | 2 | 0.02% |
| OEDC0557 | 2 | 0.02% |
| OEDC0285 | 2 | 0.02% |
| OEDC0491 | 2 | 0.02% |
| OEDC1336 | 2 | 0.02% |
| OEDC0604 | 2 | 0.02% |
| OEDC0684 | 2 | 0.02% |
| OEDC1312 | 2 | 0.02% |
| OEDC1355 | 2 | 0.02% |
| OEDC1368 | 2 | 0.02% |
| OEDC1010 | 2 | 0.02% |
| OEDC0783 | 2 | 0.02% |
| OEDC0835 | 2 | 0.02% |
| OEDC0988 | 2 | 0.02% |
| OEDC1230 | 2 | 0.02% |
| OEDC0937 | 2 | 0.02% |
| OEDC0390 | 2 | 0.02% |
| OEDC0243 | 2 | 0.02% |
| OEDC1595 | 2 | 0.02% |
| OEDC0321 | 2 | 0.02% |
| OEDC1543 | 2 | 0.02% |
| OEDC0387 | 2 | 0.02% |
| OEDC0032 | 2 | 0.02% |
| OEDC0691 | 2 | 0.02% |
| OEDC0409 | 2 | 0.02% |
| OEDC1380 | 2 | 0.02% |
| OEDC1451 | 2 | 0.02% |
| OEDC0997 | 2 | 0.02% |
| OEDC1564 | 2 | 0.02% |
| OEDC0619 | 2 | 0.02% |
| OEDC0707 | 2 | 0.02% |
| OEDC0066 | 2 | 0.02% |
| OEDC1415 | 2 | 0.02% |
| OEDC0093 | 2 | 0.02% |
| OEDC1554 | 2 | 0.02% |
| OEDC0358 | 2 | 0.02% |
| OEDC0759 | 2 | 0.02% |
| OEDC1012 | 2 | 0.02% |
| OEDC0865 | 2 | 0.02% |
| OEDC1460 | 2 | 0.02% |
| OEDC0561 | 2 | 0.02% |
| OEDC1574 | 2 | 0.02% |
| OEDC0502 | 2 | 0.02% |
| OEDC0300 | 2 | 0.02% |
| OEDC1466 | 2 | 0.02% |
| OEDC0902 | 2 | 0.02% |
| OEDC1005 | 2 | 0.02% |
| OEDC1124 | 2 | 0.02% |
| OEDC0851 | 2 | 0.02% |
| OEDC1454 | 2 | 0.02% |
| OEDC0444 | 2 | 0.02% |
| OEDC1437 | 2 | 0.02% |
| OEDC0780 | 2 | 0.02% |
| OEDC0577 | 2 | 0.02% |
| OEDC1149 | 2 | 0.02% |
| OEDC0001 | 2 | 0.02% |
| OEDC1386 | 2 | 0.02% |
| OEDC0972 | 2 | 0.02% |
| OEDC0628 | 2 | 0.02% |
| OEDC0517 | 2 | 0.02% |
| OEDC1109 | 2 | 0.02% |
| OEDC1426 | 2 | 0.02% |
| OEDC1222 | 2 | 0.02% |
| OEDC0092 | 2 | 0.02% |
| OEDC1521 | 2 | 0.02% |
| OEDC1590 | 2 | 0.02% |
| OEDC0072 | 2 | 0.02% |
| OEDC0768 | 2 | 0.02% |
| OEDC1570 | 2 | 0.02% |
| OEDC0311 | 2 | 0.02% |
| OEDC0630 | 2 | 0.02% |
| OEDC0665 | 2 | 0.02% |
| OEDC1273 | 2 | 0.02% |
| OEDC0776 | 2 | 0.02% |
| OEDC0095 | 2 | 0.02% |
| OEDC0012 | 2 | 0.02% |
| OEDC0277 | 2 | 0.02% |
| OEDC0963 | 2 | 0.02% |
| OEDC0534 | 2 | 0.02% |
| OEDC1033 | 2 | 0.02% |
| OEDC1055 | 2 | 0.02% |
| OEDC0315 | 2 | 0.02% |
| OEDC1227 | 2 | 0.02% |
| OEDC0313 | 2 | 0.02% |
| OEDC1252 | 2 | 0.02% |
| OEDC0803 | 2 | 0.02% |
| OEDC0384 | 2 | 0.02% |
| OEDC0461 | 2 | 0.02% |
| OEDC1476 | 2 | 0.02% |
| OEDC1515 | 2 | 0.02% |
| OEDC1398 | 2 | 0.02% |
| OEDC0728 | 2 | 0.02% |
| OEDC1152 | 2 | 0.02% |
| OEDC1408 | 2 | 0.02% |
| OEDC0860 | 2 | 0.02% |
| OEDC0864 | 2 | 0.02% |
| OEDC1651 | 2 | 0.02% |
| OEDC0642 | 2 | 0.02% |
| OEDC0781 | 2 | 0.02% |
| OEDC0139 | 1 | 0.01% |
| OEDC0497 | 1 | 0.01% |
| OEDC1080 | 1 | 0.01% |
| OEDC1587 | 1 | 0.01% |
| OEDC0616 | 1 | 0.01% |
| OEDC0692 | 1 | 0.01% |
| OEDC0970 | 1 | 0.01% |
| OEDC1101 | 1 | 0.01% |
| OEDC0054 | 1 | 0.01% |
| OEDC1022 | 1 | 0.01% |
| OEDC1151 | 1 | 0.01% |
| OEDC1558 | 1 | 0.01% |
| OEDC1576 | 1 | 0.01% |
| OEDC1577 | 1 | 0.01% |
| OEDC0045 | 1 | 0.01% |
| OEDC0247 | 1 | 0.01% |
| OEDC0362 | 1 | 0.01% |
| OEDC1666 | 1 | 0.01% |
| OEDC1313 | 1 | 0.01% |
| OEDC1561 | 1 | 0.01% |
| OEDC1143 | 1 | 0.01% |
| OEDC1510 | 1 | 0.01% |
| OEDC0603 | 1 | 0.01% |
| OEDC0422 | 1 | 0.01% |
| OEDC0322 | 1 | 0.01% |
| OEDC0998 | 1 | 0.01% |
| OEDC0805 | 1 | 0.01% |
| OEDC0385 | 1 | 0.01% |
| OEDC0328 | 1 | 0.01% |
| OEDC1664 | 1 | 0.01% |
| OEDC0342 | 1 | 0.01% |
| OEDC1217 | 1 | 0.01% |
| OEDC0396 | 1 | 0.01% |
| OEDC0156 | 1 | 0.01% |
| OEDC0708 | 1 | 0.01% |
| OEDC0231 | 1 | 0.01% |
| OEDC0430 | 1 | 0.01% |
| OEDC0448 | 1 | 0.01% |
| OEDC0594 | 1 | 0.01% |
| OEDC0868 | 1 | 0.01% |
| OEDC0977 | 1 | 0.01% |
| OEDC1179 | 1 | 0.01% |
| OEDC1292 | 1 | 0.01% |
| OEDC0529 | 1 | 0.01% |
| OEDC0081 | 1 | 0.01% |
| OEDC1037 | 1 | 0.01% |
| OEDC0922 | 1 | 0.01% |
| OEDC0172 | 1 | 0.01% |
| OEDC0601 | 1 | 0.01% |
| OEDC0415 | 1 | 0.01% |
| OEDC0400 | 1 | 0.01% |
| OEDC1517 | 1 | 0.01% |
| OEDC0696 | 1 | 0.01% |
| OEDC0227 | 1 | 0.01% |
| OEDC1647 | 1 | 0.01% |
| OEDC0458 | 1 | 0.01% |
| OEDC1315 | 1 | 0.01% |
| OEDC0292 | 1 | 0.01% |
| OEDC0801 | 1 | 0.01% |
| OEDC0330 | 1 | 0.01% |
| OEDC0743 | 1 | 0.01% |
| OEDC0669 | 1 | 0.01% |
| OEDC0087 | 1 | 0.01% |
| OEDC0017 | 1 | 0.01% |
| OEDC0896 | 1 | 0.01% |
| OEDC1309 | 1 | 0.01% |
| OEDC1443 | 1 | 0.01% |
| OEDC0592 | 1 | 0.01% |
| OEDC0600 | 1 | 0.01% |
| OEDC0203 | 1 | 0.01% |
| OEDC0423 | 1 | 0.01% |
| OEDC1447 | 1 | 0.01% |
| OEDC1569 | 1 | 0.01% |
| OEDC0750 | 1 | 0.01% |
| OEDC1518 | 1 | 0.01% |
| OEDC1169 | 1 | 0.01% |
| OEDC0484 | 1 | 0.01% |
| OEDC0959 | 1 | 0.01% |
| OEDC1471 | 1 | 0.01% |
| OEDC0041 | 1 | 0.01% |
| OEDC1625 | 1 | 0.01% |
| OEDC1607 | 1 | 0.01% |
| OEDC1661 | 1 | 0.01% |
| OEDC0899 | 1 | 0.01% |
| OEDC0278 | 1 | 0.01% |
| OEDC0903 | 1 | 0.01% |
| OEDC0068 | 1 | 0.01% |
| OEDC1246 | 1 | 0.01% |
| OEDC1209 | 1 | 0.01% |
| OEDC0475 | 1 | 0.01% |
| OEDC1165 | 1 | 0.01% |
| OEDC0583 | 1 | 0.01% |
| OEDC1195 | 1 | 0.01% |
| OEDC0455 | 1 | 0.01% |
| OEDC1662 | 1 | 0.01% |
| OEDC0369 | 1 | 0.01% |
| OEDC1011 | 1 | 0.01% |
| OEDC1242 | 1 | 0.01% |
| OEDC0284 | 1 | 0.01% |
| OEDC1137 | 1 | 0.01% |
| OEDC0361 | 1 | 0.01% |
| OEDC0767 | 1 | 0.01% |
| OEDC0987 | 1 | 0.01% |
| OEDC1557 | 1 | 0.01% |
| OEDC0686 | 1 | 0.01% |
| OEDC0799 | 1 | 0.01% |
| OEDC1047 | 1 | 0.01% |
| OEDC1333 | 1 | 0.01% |
| OEDC0549 | 1 | 0.01% |
| OEDC1001 | 1 | 0.01% |
| OEDC0451 | 1 | 0.01% |
| OEDC1092 | 1 | 0.01% |
| OEDC1432 | 1 | 0.01% |
| OEDC0111 | 1 | 0.01% |
| OEDC1358 | 1 | 0.01% |
| OEDC0234 | 1 | 0.01% |
| OEDC0112 | 1 | 0.01% |
| OEDC0674 | 1 | 0.01% |
| OEDC0717 | 1 | 0.01% |
| OEDC0030 | 1 | 0.01% |
| OEDC0215 | 1 | 0.01% |
| OEDC1031 | 1 | 0.01% |
| OEDC1192 | 1 | 0.01% |
| OEDC1133 | 1 | 0.01% |
| OEDC0488 | 1 | 0.01% |
| OEDC1304 | 1 | 0.01% |
| OEDC1302 | 1 | 0.01% |
| OEDC0191 | 1 | 0.01% |
| OEDC0274 | 1 | 0.01% |
| OEDC0414 | 1 | 0.01% |
| OEDC0934 | 1 | 0.01% |
| OEDC0294 | 1 | 0.01% |
| OEDC0114 | 1 | 0.01% |
| OEDC0844 | 1 | 0.01% |
| OEDC1383 | 1 | 0.01% |
| OEDC0984 | 1 | 0.01% |
| OEDC1185 | 1 | 0.01% |
| OEDC0774 | 1 | 0.01% |
| OEDC0861 | 1 | 0.01% |
| OEDC1009 | 1 | 0.01% |
| OEDC0904 | 1 | 0.01% |
| OEDC0492 | 1 | 0.01% |
| OEDC1599 | 1 | 0.01% |
| OEDC0862 | 1 | 0.01% |
| OEDC0197 | 1 | 0.01% |
| OEDC0980 | 1 | 0.01% |
| OEDC0632 | 1 | 0.01% |
| OEDC0503 | 1 | 0.01% |
| OEDC1418 | 1 | 0.01% |
| OEDC1110 | 1 | 0.01% |
| OEDC1563 | 1 | 0.01% |
| OEDC0471 | 1 | 0.01% |
| OEDC1632 | 1 | 0.01% |
| OEDC0546 | 1 | 0.01% |
| OEDC1036 | 1 | 0.01% |
| OEDC1284 | 1 | 0.01% |
| OEDC0638 | 1 | 0.01% |
| OEDC0408 | 1 | 0.01% |
| OEDC0777 | 1 | 0.01% |
| OEDC0120 | 1 | 0.01% |
| OEDC0956 | 1 | 0.01% |
| OEDC0280 | 1 | 0.01% |
| OEDC0075 | 1 | 0.01% |
| OEDC0611 | 1 | 0.01% |
| OEDC0371 | 1 | 0.01% |
| OEDC0590 | 1 | 0.01% |
| OEDC1402 | 1 | 0.01% |
| OEDC1074 | 1 | 0.01% |
| OEDC0439 | 1 | 0.01% |
| OEDC0929 | 1 | 0.01% |
| OEDC0149 | 1 | 0.01% |
| OEDC0858 | 1 | 0.01% |
| OEDC0567 | 1 | 0.01% |
| OEDC1126 | 1 | 0.01% |
| OEDC0100 | 1 | 0.01% |
| OEDC0661 | 1 | 0.01% |
| OEDC1329 | 1 | 0.01% |
| OEDC0061 | 1 | 0.01% |
| OEDC0528 | 1 | 0.01% |
| OEDC0726 | 1 | 0.01% |
| OEDC0876 | 1 | 0.01% |
| OEDC1339 | 1 | 0.01% |
| OEDC0474 | 1 | 0.01% |
| OEDC0169 | 1 | 0.01% |
| OEDC0176 | 1 | 0.01% |
| OEDC0452 | 1 | 0.01% |
| OEDC1553 | 1 | 0.01% |
| OEDC0554 | 1 | 0.01% |
| OEDC0399 | 1 | 0.01% |
| OEDC0834 | 1 | 0.01% |
| OEDC0663 | 1 | 0.01% |
| OEDC0297 | 1 | 0.01% |
| OEDC1653 | 1 | 0.01% |
| OEDC0253 | 1 | 0.01% |
| OEDC1349 | 1 | 0.01% |
| OEDC0558 | 1 | 0.01% |
| OEDC1189 | 1 | 0.01% |
| OEDC0063 | 1 | 0.01% |
| OEDC0853 | 1 | 0.01% |
| OEDC1068 | 1 | 0.01% |
| OEDC0162 | 1 | 0.01% |
| OEDC1135 | 1 | 0.01% |
| OEDC0386 | 1 | 0.01% |
| OEDC0267 | 1 | 0.01% |
| OEDC0510 | 1 | 0.01% |
| OEDC1502 | 1 | 0.01% |
| OEDC1504 | 1 | 0.01% |
| OEDC1596 | 1 | 0.01% |
| OEDC0666 | 1 | 0.01% |
| OEDC0839 | 1 | 0.01% |
| OEDC0090 | 1 | 0.01% |
| OEDC1160 | 1 | 0.01% |
| OEDC0073 | 1 | 0.01% |
| OEDC0664 | 1 | 0.01% |
| OEDC0067 | 1 | 0.01% |
| OEDC0840 | 1 | 0.01% |
| OEDC0144 | 1 | 0.01% |
| OEDC0637 | 1 | 0.01% |
| OEDC0718 | 1 | 0.01% |
| OEDC0184 | 1 | 0.01% |
| OEDC1106 | 1 | 0.01% |
| OEDC0752 | 1 | 0.01% |
| OEDC0891 | 1 | 0.01% |
| OEDC1098 | 1 | 0.01% |
| OEDC0016 | 1 | 0.01% |
| OEDC1637 | 1 | 0.01% |
| OEDC1413 | 1 | 0.01% |
| OEDC1424 | 1 | 0.01% |
| OEDC0352 | 1 | 0.01% |
| OEDC1212 | 1 | 0.01% |
| OEDC0789 | 1 | 0.01% |
| OEDC0877 | 1 | 0.01% |
| OEDC1387 | 1 | 0.01% |
| OEDC0116 | 1 | 0.01% |
| OEDC0539 | 1 | 0.01% |
| OEDC1316 | 1 | 0.01% |
| OEDC0246 | 1 | 0.01% |
| OEDC0629 | 1 | 0.01% |
| OEDC1029 | 1 | 0.01% |
| OEDC1397 | 1 | 0.01% |
| OEDC0547 | 1 | 0.01% |
| OEDC0533 | 1 | 0.01% |
| OEDC1538 | 1 | 0.01% |
| OEDC1018 | 1 | 0.01% |
| OEDC1340 | 1 | 0.01% |
| OEDC1428 | 1 | 0.01% |
| OEDC0990 | 1 | 0.01% |
| OEDC0550 | 1 | 0.01% |
| OEDC0060 | 1 | 0.01% |
| OEDC0831 | 1 | 0.01% |
| OEDC0650 | 1 | 0.01% |
| OEDC0504 | 1 | 0.01% |
| OEDC1548 | 1 | 0.01% |
| OEDC1406 | 1 | 0.01% |
| OEDC1083 | 1 | 0.01% |
| OEDC0198 | 1 | 0.01% |
| OEDC0644 | 1 | 0.01% |
| OEDC0123 | 1 | 0.01% |
| OEDC0223 | 1 | 0.01% |
| OEDC0498 | 1 | 0.01% |
| OEDC1207 | 1 | 0.01% |
| OEDC0250 | 1 | 0.01% |
| OEDC1648 | 1 | 0.01% |
| OEDC1125 | 1 | 0.01% |
| OEDC0775 | 1 | 0.01% |
| OEDC0622 | 1 | 0.01% |
| OEDC0404 | 1 | 0.01% |
| OEDC0819 | 1 | 0.01% |
| OEDC1394 | 1 | 0.01% |
| OEDC0795 | 1 | 0.01% |
| OEDC0367 | 1 | 0.01% |
| OEDC1472 | 1 | 0.01% |
| OEDC1017 | 1 | 0.01% |
| OEDC0403 | 1 | 0.01% |
| OEDC0569 | 1 | 0.01% |
| OEDC0916 | 1 | 0.01% |
| OEDC0617 | 1 | 0.01% |
| OEDC0833 | 1 | 0.01% |
| OEDC0357 | 1 | 0.01% |
| OEDC0209 | 1 | 0.01% |
| OEDC0058 | 1 | 0.01% |
| OEDC0932 | 1 | 0.01% |
| OEDC1332 | 1 | 0.01% |
| OEDC0719 | 1 | 0.01% |
| OEDC0895 | 1 | 0.01% |
| OEDC0523 | 1 | 0.01% |
| OEDC0302 | 1 | 0.01% |
| OEDC0509 | 1 | 0.01% |
| OEDC1258 | 1 | 0.01% |
| OEDC1267 | 1 | 0.01% |
| OEDC0745 | 1 | 0.01% |
| OEDC1468 | 1 | 0.01% |
| OEDC0854 | 1 | 0.01% |
| OEDC1119 | 1 | 0.01% |
| OEDC0333 | 1 | 0.01% |
| OEDC0235 | 1 | 0.01% |
| OEDC0296 | 1 | 0.01% |
| OEDC1403 | 1 | 0.01% |
| OEDC1197 | 1 | 0.01% |
| OEDC0640 | 1 | 0.01% |
| OEDC0161 | 1 | 0.01% |
| OEDC1112 | 1 | 0.01% |
| OEDC0634 | 1 | 0.01% |
| OEDC1620 | 1 | 0.01% |
| OEDC1103 | 1 | 0.01% |
| OEDC1529 | 1 | 0.01% |
| OEDC1084 | 1 | 0.01% |
| OEDC0679 | 1 | 0.01% |
| OEDC1484 | 1 | 0.01% |
| OEDC0326 | 1 | 0.01% |
| OEDC0734 | 1 | 0.01% |
| OEDC1627 | 1 | 0.01% |
| OEDC0105 | 1 | 0.01% |
| OEDC1202 | 1 | 0.01% |
| OEDC1407 | 1 | 0.01% |
| OEDC1278 | 1 | 0.01% |
| OEDC0132 | 1 | 0.01% |
| OEDC1591 | 1 | 0.01% |
| OEDC1281 | 1 | 0.01% |
| OEDC1290 | 1 | 0.01% |
| OEDC1237 | 1 | 0.01% |
| OEDC0643 | 1 | 0.01% |
| OEDC1235 | 1 | 0.01% |
| OEDC1630 | 1 | 0.01% |
| OEDC1253 | 1 | 0.01% |
| OEDC0527 | 1 | 0.01% |
| OEDC0994 | 1 | 0.01% |
| OEDC0426 | 1 | 0.01% |
| OEDC1294 | 1 | 0.01% |
| OEDC0407 | 1 | 0.01% |
| OEDC0709 | 1 | 0.01% |
| OEDC1525 | 1 | 0.01% |
| OEDC0304 | 1 | 0.01% |
| OEDC0146 | 1 | 0.01% |
| OEDC0706 | 1 | 0.01% |
| OEDC0543 | 1 | 0.01% |
| OEDC0938 | 1 | 0.01% |
| OEDC0129 | 1 | 0.01% |
| OEDC0393 | 1 | 0.01% |
| OEDC0119 | 1 | 0.01% |
| OEDC0677 | 1 | 0.01% |
| OEDC0516 | 1 | 0.01% |
| OEDC1174 | 1 | 0.01% |
| OEDC0749 | 1 | 0.01% |
| OEDC1376 | 1 | 0.01% |
| OEDC1633 | 1 | 0.01% |
| OEDC0483 | 1 | 0.01% |
| OEDC0883 | 1 | 0.01% |
| OEDC0919 | 1 | 0.01% |
| OEDC0211 | 1 | 0.01% |
| OEDC0440 | 1 | 0.01% |
| OEDC1208 | 1 | 0.01% |
| OEDC1086 | 1 | 0.01% |
| OEDC0094 | 1 | 0.01% |
| OEDC0731 | 1 | 0.01% |
| OEDC0425 | 1 | 0.01% |
| OEDC0435 | 1 | 0.01% |
| OEDC1497 | 1 | 0.01% |
| OEDC0099 | 1 | 0.01% |
| OEDC0732 | 1 | 0.01% |
| OEDC1262 | 1 | 0.01% |
| OEDC1513 | 1 | 0.01% |
| OEDC0879 | 1 | 0.01% |
| OEDC0522 | 1 | 0.01% |
| OEDC0986 | 1 | 0.01% |
| OEDC1245 | 1 | 0.01% |
| OEDC1102 | 1 | 0.01% |
| OEDC0153 | 1 | 0.01% |
| OEDC0704 | 1 | 0.01% |
| OEDC1608 | 1 | 0.01% |
| OEDC0989 | 1 | 0.01% |
| OEDC1065 | 1 | 0.01% |
| OEDC1343 | 1 | 0.01% |
| OEDC1147 | 1 | 0.01% |
| OEDC0177 | 1 | 0.01% |
| OEDC1388 | 1 | 0.01% |
| OEDC0014 | 1 | 0.01% |
| OEDC0490 | 1 | 0.01% |
| OEDC0034 | 1 | 0.01% |
| OEDC0507 | 1 | 0.01% |
| OEDC0453 | 1 | 0.01% |
| OEDC0609 | 1 | 0.01% |
| OEDC0389 | 1 | 0.01% |
| OEDC1038 | 1 | 0.01% |
| OEDC0532 | 1 | 0.01% |
| OEDC0218 | 1 | 0.01% |
| OEDC1121 | 1 | 0.01% |
| OEDC1204 | 1 | 0.01% |
| OEDC0798 | 1 | 0.01% |
| OEDC1532 | 1 | 0.01% |
| OEDC0812 | 1 | 0.01% |
| OEDC0508 | 1 | 0.01% |
| OEDC0102 | 1 | 0.01% |
| OEDC0681 | 1 | 0.01% |
| OEDC0457 | 1 | 0.01% |
| OEDC1168 | 1 | 0.01% |
| OEDC0544 | 1 | 0.01% |
| OEDC0668 | 1 | 0.01% |
| OEDC1450 | 1 | 0.01% |
| OEDC0746 | 1 | 0.01% |
| OEDC0825 | 1 | 0.01% |
| OEDC0319 | 1 | 0.01% |
| OEDC1093 | 1 | 0.01% |
| OEDC0117 | 1 | 0.01% |
| OEDC1364 | 1 | 0.01% |
| OEDC1344 | 1 | 0.01% |
| OEDC0494 | 1 | 0.01% |
| OEDC0863 | 1 | 0.01% |
| OEDC1091 | 1 | 0.01% |
| OEDC1550 | 1 | 0.01% |
| OEDC1655 | 1 | 0.01% |
| OEDC0560 | 1 | 0.01% |
| OEDC0936 | 1 | 0.01% |
| OEDC1366 | 1 | 0.01% |
| OEDC1298 | 1 | 0.01% |
| OEDC1455 | 1 | 0.01% |
| OEDC0125 | 1 | 0.01% |
| OEDC0003 | 1 | 0.01% |
| OEDC0797 | 1 | 0.01% |
| OEDC0438 | 1 | 0.01% |
| OEDC0413 | 1 | 0.01% |
| OEDC0270 | 1 | 0.01% |
| OEDC0495 | 1 | 0.01% |
| OEDC0338 | 1 | 0.01% |
| OEDC1301 | 1 | 0.01% |
| OEDC1531 | 1 | 0.01% |
| OEDC0186 | 1 | 0.01% |
| OEDC1544 | 1 | 0.01% |
| OEDC1238 | 1 | 0.01% |
| OEDC1271 | 1 | 0.01% |
| OEDC1458 | 1 | 0.01% |
| OEDC0348 | 1 | 0.01% |
| OEDC1492 | 1 | 0.01% |
| OEDC0224 | 1 | 0.01% |
| OEDC0552 | 1 | 0.01% |
| OEDC1491 | 1 | 0.01% |
| OEDC0900 | 1 | 0.01% |
| OEDC1076 | 1 | 0.01% |
| OEDC0519 | 1 | 0.01% |
| OEDC0748 | 1 | 0.01% |
| OEDC1042 | 1 | 0.01% |
| OEDC1571 | 1 | 0.01% |
| OEDC1631 | 1 | 0.01% |
| OEDC1268 | 1 | 0.01% |
| OEDC1233 | 1 | 0.01% |
| OEDC1392 | 1 | 0.01% |
| OEDC0695 | 1 | 0.01% |
| OEDC1385 | 1 | 0.01% |
| OEDC1469 | 1 | 0.01% |
| OEDC1337 | 1 | 0.01% |
| OEDC1425 | 1 | 0.01% |
| OEDC0701 | 1 | 0.01% |
| OEDC1552 | 1 | 0.01% |
| OEDC0740 | 1 | 0.01% |
| OEDC1156 | 1 | 0.01% |
| OEDC0921 | 1 | 0.01% |
| OEDC1075 | 1 | 0.01% |
| OEDC1226 | 1 | 0.01% |
| OEDC1656 | 1 | 0.01% |
| OEDC0725 | 1 | 0.01% |
| OEDC0173 | 1 | 0.01% |
| OEDC1410 | 1 | 0.01% |
| OEDC0195 | 1 | 0.01% |
| OEDC0021 | 1 | 0.01% |
| OEDC0411 | 1 | 0.01% |
| OEDC0975 | 1 | 0.01% |
| OEDC0018 | 1 | 0.01% |
| OEDC1232 | 1 | 0.01% |
| OEDC1199 | 1 | 0.01% |
| OEDC0303 | 1 | 0.01% |
| OEDC0506 | 1 | 0.01% |
| OEDC0263 | 1 | 0.01% |
| OEDC1141 | 1 | 0.01% |
| OEDC1206 | 1 | 0.01% |
| OEDC0500 | 1 | 0.01% |
| OEDC0468 | 1 | 0.01% |
| OEDC0318 | 1 | 0.01% |
| OEDC0388 | 1 | 0.01% |
| OEDC0588 | 1 | 0.01% |
| OEDC0727 | 1 | 0.01% |
| OEDC0564 | 1 | 0.01% |
| OEDC1025 | 1 | 0.01% |
| OEDC0531 | 1 | 0.01% |
| OEDC0341 | 1 | 0.01% |
| OEDC0559 | 1 | 0.01% |
| OEDC0006 | 1 | 0.01% |
| OEDC1427 | 1 | 0.01% |
| OEDC0207 | 1 | 0.01% |
| OEDC0130 | 1 | 0.01% |
| OEDC0496 | 1 | 0.01% |
| OEDC0991 | 1 | 0.01% |
| OEDC0237 | 1 | 0.01% |
| OEDC1585 | 1 | 0.01% |
| OEDC0832 | 1 | 0.01% |
| OEDC0729 | 1 | 0.01% |
| OEDC1537 | 1 | 0.01% |
| OEDC1308 | 1 | 0.01% |
| OEDC1588 | 1 | 0.01% |
| OEDC1265 | 1 | 0.01% |
| OEDC1433 | 1 | 0.01% |
| OEDC1509 | 1 | 0.01% |
| OEDC1201 | 1 | 0.01% |
| OEDC1541 | 1 | 0.01% |
| OEDC1218 | 1 | 0.01% |
| OEDC1373 | 1 | 0.01% |
| OEDC0028 | 1 | 0.01% |
| OEDC0872 | 1 | 0.01% |
| OEDC1250 | 1 | 0.01% |
| OEDC0185 | 1 | 0.01% |
| OEDC0910 | 1 | 0.01% |
| OEDC0150 | 1 | 0.01% |
| OEDC0623 | 1 | 0.01% |
| OEDC1341 | 1 | 0.01% |
| OEDC0698 | 1 | 0.01% |
| OEDC0942 | 1 | 0.01% |
| OEDC0595 | 1 | 0.01% |
| OEDC0314 | 1 | 0.01% |
| OEDC1371 | 1 | 0.01% |
| OEDC1073 | 1 | 0.01% |
| OEDC0788 | 1 | 0.01% |
| OEDC1303 | 1 | 0.01% |
| OEDC1462 | 1 | 0.01% |
| OEDC0657 | 1 | 0.01% |
| OEDC1122 | 1 | 0.01% |
| OEDC0410 | 1 | 0.01% |
| OEDC1142 | 1 | 0.01% |
| OEDC0204 | 1 | 0.01% |
| OEDC1330 | 1 | 0.01% |
| OEDC0697 | 1 | 0.01% |
| OEDC0347 | 1 | 0.01% |
| OEDC0981 | 1 | 0.01% |
| OEDC0979 | 1 | 0.01% |
| OEDC0057 | 1 | 0.01% |
| OEDC1287 | 1 | 0.01% |
| OEDC1079 | 1 | 0.01% |
| OEDC1275 | 1 | 0.01% |
| OEDC0166 | 1 | 0.01% |
| OEDC0269 | 1 | 0.01% |
| OEDC1500 | 1 | 0.01% |
| OEDC1046 | 1 | 0.01% |
| OEDC1013 | 1 | 0.01% |
| OEDC1446 | 1 | 0.01% |
| OEDC0818 | 1 | 0.01% |
| OEDC1030 | 1 | 0.01% |
| OEDC1603 | 1 | 0.01% |
| OEDC0055 | 1 | 0.01% |
| OEDC0327 | 1 | 0.01% |
| OEDC1331 | 1 | 0.01% |
| OEDC1352 | 1 | 0.01% |
| OEDC0232 | 1 | 0.01% |
| OEDC1023 | 1 | 0.01% |
| OEDC0631 | 1 | 0.01% |
| OEDC0221 | 1 | 0.01% |
| OEDC0038 | 1 | 0.01% |
| OEDC1205 | 1 | 0.01% |
| OEDC0128 | 1 | 0.01% |
| OEDC1213 | 1 | 0.01% |
| OEDC1478 | 1 | 0.01% |
| OEDC0613 | 1 | 0.01% |
| OEDC1511 | 1 | 0.01% |
| OEDC0935 | 1 | 0.01% |
| OEDC0301 | 1 | 0.01% |
| OEDC0416 | 1 | 0.01% |
| OEDC0906 | 1 | 0.01% |
| OEDC0954 | 1 | 0.01% |
| OEDC0967 | 1 | 0.01% |
| OEDC1389 | 1 | 0.01% |
| OEDC0193 | 1 | 0.01% |
| OEDC1264 | 1 | 0.01% |
| OEDC1321 | 1 | 0.01% |
| OEDC0272 | 1 | 0.01% |
| OEDC0928 | 1 | 0.01% |
| OEDC1318 | 1 | 0.01% |
| OEDC1044 | 1 | 0.01% |
| OEDC0312 | 1 | 0.01% |
| OEDC0821 | 1 | 0.01% |
| OEDC1334 | 1 | 0.01% |
| OEDC1095 | 1 | 0.01% |
| OEDC0194 | 1 | 0.01% |
| OEDC0964 | 1 | 0.01% |
| OEDC1409 | 1 | 0.01% |
| OEDC1117 | 1 | 0.01% |
| OEDC1113 | 1 | 0.01% |
| OEDC0469 | 1 | 0.01% |
| OEDC0737 | 1 | 0.01% |
| OEDC1320 | 1 | 0.01% |
| OEDC0454 | 1 | 0.01% |
| OEDC0134 | 1 | 0.01% |
| OEDC1391 | 1 | 0.01% |
| OEDC0582 | 1 | 0.01% |
| OEDC0010 | 1 | 0.01% |
| OEDC1049 | 1 | 0.01% |
| OEDC1536 | 1 | 0.01% |
| OEDC0913 | 1 | 0.01% |
| OEDC1032 | 1 | 0.01% |
| OEDC0606 | 1 | 0.01% |
| OEDC0155 | 1 | 0.01% |
| OEDC0646 | 1 | 0.01% |
| OEDC0238 | 1 | 0.01% |
| OEDC0168 | 1 | 0.01% |
| OEDC0973 | 1 | 0.01% |
| OEDC0927 | 1 | 0.01% |
| OEDC1512 | 1 | 0.01% |
| OEDC0160 | 1 | 0.01% |
| OEDC0189 | 1 | 0.01% |
| OEDC1256 | 1 | 0.01% |
| OEDC1566 | 1 | 0.01% |
| OEDC0814 | 1 | 0.01% |
| OEDC0200 | 1 | 0.01% |
| OEDC1377 | 1 | 0.01% |
| OEDC1592 | 1 | 0.01% |
| OEDC1435 | 1 | 0.01% |
| OEDC0571 | 1 | 0.01% |
| OEDC0096 | 1 | 0.01% |
| OEDC1089 | 1 | 0.01% |
| OEDC0271 | 1 | 0.01% |
| OEDC1118 | 1 | 0.01% |
| OEDC1255 | 1 | 0.01% |
| OEDC0459 | 1 | 0.01% |
| OEDC1555 | 1 | 0.01% |
| OEDC0658 | 1 | 0.01% |
| OEDC0724 | 1 | 0.01% |
| OEDC1171 | 1 | 0.01% |
| OEDC1229 | 1 | 0.01% |
| OEDC0836 | 1 | 0.01% |
| OEDC0307 | 1 | 0.01% |
| OEDC0535 | 1 | 0.01% |
| OEDC0576 | 1 | 0.01% |
| OEDC0976 | 1 | 0.01% |
| OEDC1663 | 1 | 0.01% |
| OEDC1612 | 1 | 0.01% |
| OEDC1361 | 1 | 0.01% |
| OEDC0074 | 1 | 0.01% |
| OEDC0473 | 1 | 0.01% |
| OEDC0420 | 1 | 0.01% |
| OEDC1154 | 1 | 0.01% |
| OEDC0286 | 1 | 0.01% |
| OEDC0442 | 1 | 0.01% |
| OEDC0700 | 1 | 0.01% |
| OEDC0098 | 1 | 0.01% |
| OEDC0950 | 1 | 0.01% |
| OEDC1393 | 1 | 0.01% |
| OEDC0009 | 1 | 0.01% |
| OEDC1034 | 1 | 0.01% |
| OEDC1654 | 1 | 0.01% |
| OEDC1190 | 1 | 0.01% |
| OEDC0276 | 1 | 0.01% |
| OEDC0287 | 1 | 0.01% |
| OEDC1495 | 1 | 0.01% |
| OEDC0107 | 1 | 0.01% |
| OEDC1646 | 1 | 0.01% |
| OEDC1441 | 1 | 0.01% |
| OEDC0019 | 1 | 0.01% |
| OEDC1356 | 1 | 0.01% |
| OEDC0892 | 1 | 0.01% |
| OEDC1254 | 1 | 0.01% |
| OEDC1291 | 1 | 0.01% |
| OEDC1423 | 1 | 0.01% |
| OEDC1070 | 1 | 0.01% |
| OEDC0869 | 1 | 0.01% |
| OEDC1533 | 1 | 0.01% |
| OEDC1153 | 1 | 0.01% |
| OEDC0049 | 1 | 0.01% |
| OEDC0295 | 1 | 0.01% |
| OEDC1257 | 1 | 0.01% |
| OEDC0225 | 1 | 0.01% |
| OEDC1589 | 1 | 0.01% |
| OEDC0521 | 1 | 0.01% |
| OEDC0871 | 1 | 0.01% |
| OEDC0791 | 1 | 0.01% |
| OEDC0433 | 1 | 0.01% |
| OEDC0115 | 1 | 0.01% |
| OEDC0262 | 1 | 0.01% |
| OEDC1167 | 1 | 0.01% |
| OEDC0530 | 1 | 0.01% |
| OEDC0615 | 1 | 0.01% |
| OEDC0148 | 1 | 0.01% |
| OEDC0512 | 1 | 0.01% |
| OEDC1266 | 1 | 0.01% |
| OEDC1638 | 1 | 0.01% |
| OEDC0145 | 1 | 0.01% |
| OEDC0915 | 1 | 0.01% |
| OEDC0288 | 1 | 0.01% |
| OEDC0419 | 1 | 0.01% |
| OEDC0179 | 1 | 0.01% |
| OEDC0398 | 1 | 0.01% |
| OEDC1028 | 1 | 0.01% |
| OEDC1164 | 1 | 0.01% |
| OEDC0808 | 1 | 0.01% |
| OEDC1231 | 1 | 0.01% |
| OEDC0849 | 1 | 0.01% |
| OEDC1507 | 1 | 0.01% |
| OEDC0353 | 1 | 0.01% |
| OEDC1307 | 1 | 0.01% |
| OEDC0346 | 1 | 0.01% |
| OEDC1067 | 1 | 0.01% |
| OEDC0810 | 1 | 0.01% |
| OEDC0714 | 1 | 0.01% |
| OEDC1354 | 1 | 0.01% |
| OEDC0022 | 1 | 0.01% |
| OEDC0240 | 1 | 0.01% |
| OEDC1382 | 1 | 0.01% |
| OEDC1041 | 1 | 0.01% |
| OEDC0248 | 1 | 0.01% |
| OEDC0816 | 1 | 0.01% |
| OEDC1635 | 1 | 0.01% |
| OEDC1381 | 1 | 0.01% |
| OEDC1064 | 1 | 0.01% |
| OEDC0140 | 1 | 0.01% |
| OEDC1003 | 1 | 0.01% |
| OEDC0645 | 1 | 0.01% |
| OEDC0884 | 1 | 0.01% |
| OEDC0802 | 1 | 0.01% |
| OEDC0040 | 1 | 0.01% |
| OEDC0572 | 1 | 0.01% |
| OEDC0580 | 1 | 0.01% |
| OEDC1096 | 1 | 0.01% |
| OEDC1488 | 1 | 0.01% |
| OEDC0711 | 1 | 0.01% |
| OEDC0713 | 1 | 0.01% |
| OEDC0660 | 1 | 0.01% |
| OEDC0365 | 1 | 0.01% |
| OEDC1061 | 1 | 0.01% |
| OEDC0266 | 1 | 0.01% |
| OEDC0187 | 1 | 0.01% |
| OEDC0599 | 1 | 0.01% |
| OEDC1059 | 1 | 0.01% |
| OEDC0940 | 1 | 0.01% |
| OEDC0924 | 1 | 0.01% |
| OEDC0222 | 1 | 0.01% |
| OEDC1345 | 1 | 0.01% |
| OEDC1565 | 1 | 0.01% |
| OEDC0647 | 1 | 0.01% |
| OEDC1311 | 1 | 0.01% |
| OEDC1325 | 1 | 0.01% |
| OEDC0381 | 1 | 0.01% |
| OEDC1058 | 1 | 0.01% |
| OEDC0680 | 1 | 0.01% |
| OEDC1372 | 1 | 0.01% |
| OEDC0351 | 1 | 0.01% |
| OEDC0366 | 1 | 0.01% |
| OEDC1132 | 1 | 0.01% |
| OEDC0015 | 1 | 0.01% |
| OEDC1090 | 1 | 0.01% |
| OEDC0412 | 1 | 0.01% |
| OEDC0787 | 1 | 0.01% |
| OEDC1474 | 1 | 0.01% |
| OEDC1054 | 1 | 0.01% |
| OEDC0436 | 1 | 0.01% |
| OEDC1260 | 1 | 0.01% |
| OEDC1660 | 1 | 0.01% |
| OEDC0037 | 1 | 0.01% |
| OEDC1008 | 1 | 0.01% |
| OEDC1280 | 1 | 0.01% |
| OEDC1617 | 1 | 0.01% |
| OEDC0949 | 1 | 0.01% |
| OEDC0136 | 1 | 0.01% |
| OEDC0770 | 1 | 0.01% |
| OEDC0486 | 1 | 0.01% |
| OEDC1584 | 1 | 0.01% |
| OEDC0044 | 1 | 0.01% |
| OEDC1198 | 1 | 0.01% |
| OEDC0080 | 1 | 0.01% |
| OEDC0806 | 1 | 0.01% |
| OEDC0428 | 1 | 0.01% |
| OEDC0201 | 1 | 0.01% |
| OEDC1173 | 1 | 0.01% |
| OEDC1499 | 1 | 0.01% |
| OEDC0339 | 1 | 0.01% |
| OEDC0208 | 1 | 0.01% |
| OEDC0667 | 1 | 0.01% |
| OEDC1611 | 1 | 0.01% |
| OEDC1463 | 1 | 0.01% |
| OEDC0969 | 1 | 0.01% |
| OEDC1244 | 1 | 0.01% |
| OEDC0581 | 1 | 0.01% |
| OEDC0035 | 1 | 0.01% |
| OEDC0843 | 1 | 0.01% |
| OEDC0254 | 1 | 0.01% |
| OEDC1353 | 1 | 0.01% |
| OEDC0513 | 1 | 0.01% |
| OEDC0405 | 1 | 0.01% |
| OEDC1610 | 1 | 0.01% |
| OEDC1560 | 1 | 0.01% |
| OEDC0960 | 1 | 0.01% |
| OEDC0699 | 1 | 0.01% |
| OEDC0551 | 1 | 0.01% |
| OEDC0417 | 1 | 0.01% |
| OEDC0233 | 1 | 0.01% |
| OEDC0370 | 1 | 0.01% |
| OEDC1615 | 1 | 0.01% |
| OEDC1221 | 1 | 0.01% |
| OEDC0742 | 1 | 0.01% |
| OEDC0023 | 1 | 0.01% |
| OEDC1470 | 1 | 0.01% |
| OEDC1027 | 1 | 0.01% |
| OEDC1279 | 1 | 0.01% |
| OEDC0374 | 1 | 0.01% |
| OEDC1559 | 1 | 0.01% |
| OEDC1157 | 1 | 0.01% |
| OEDC1115 | 1 | 0.01% |
| OEDC1069 | 1 | 0.01% |
| OEDC0716 | 1 | 0.01% |
| OEDC1481 | 1 | 0.01% |
| OEDC0757 | 1 | 0.01% |
| OEDC0586 | 1 | 0.01% |
| OEDC1461 | 1 | 0.01% |
| OEDC0355 | 1 | 0.01% |
| OEDC1498 | 1 | 0.01% |
| OEDC0127 | 1 | 0.01% |
| OEDC1445 | 1 | 0.01% |
| OEDC0555 | 1 | 0.01% |
| OEDC1114 | 1 | 0.01% |
| OEDC1182 | 1 | 0.01% |
| OEDC1130 | 1 | 0.01% |
| OEDC0974 | 1 | 0.01% |
| OEDC0485 | 1 | 0.01% |
| OEDC0264 | 1 | 0.01% |
| OEDC1479 | 1 | 0.01% |
| OEDC1477 | 1 | 0.01% |
| OEDC1236 | 1 | 0.01% |
| OEDC1623 | 1 | 0.01% |
| OEDC0983 | 1 | 0.01% |
| OEDC0520 | 1 | 0.01% |
| OEDC0943 | 1 | 0.01% |
| OEDC1088 | 1 | 0.01% |
| OEDC0541 | 1 | 0.01% |
| OEDC1057 | 1 | 0.01% |
| OEDC0769 | 1 | 0.01% |
| OEDC0397 | 1 | 0.01% |
| OEDC0589 | 1 | 0.01% |
| OEDC0489 | 1 | 0.01% |
| OEDC0824 | 1 | 0.01% |
| OEDC1378 | 1 | 0.01% |
| OEDC0077 | 1 | 0.01% |
| OEDC1272 | 1 | 0.01% |
| OEDC0574 | 1 | 0.01% |
| OEDC1351 | 1 | 0.01% |
| OEDC0736 | 1 | 0.01% |
| OEDC0478 | 1 | 0.01% |
| OEDC1621 | 1 | 0.01% |
| OEDC1269 | 1 | 0.01% |
| OEDC0678 | 1 | 0.01% |
| OEDC1289 | 1 | 0.01% |
| OEDC1503 | 1 | 0.01% |
| OEDC0456 | 1 | 0.01% |
| OEDC1580 | 1 | 0.01% |
| OEDC1505 | 1 | 0.01% |
| OEDC0754 | 1 | 0.01% |
| OEDC0968 | 1 | 0.01% |
| OEDC0487 | 1 | 0.01% |
| OEDC0675 | 1 | 0.01% |
| OEDC0545 | 1 | 0.01% |
| OEDC0007 | 1 | 0.01% |
| OEDC0482 | 1 | 0.01% |
| OEDC0563 | 1 | 0.01% |
| OEDC0024 | 1 | 0.01% |
| OEDC0170 | 1 | 0.01% |
| OEDC0259 | 1 | 0.01% |
| OEDC0670 | 1 | 0.01% |
| OEDC0779 | 1 | 0.01% |
| OEDC0793 | 1 | 0.01% |
| OEDC0852 | 1 | 0.01% |
| OEDC0710 | 1 | 0.01% |
| OEDC0373 | 1 | 0.01% |
| OEDC1609 | 1 | 0.01% |
| OEDC0070 | 1 | 0.01% |
| OEDC1527 | 1 | 0.01% |
| OEDC0505 | 1 | 0.01% |
| OEDC0584 | 1 | 0.01% |
| OEDC0206 | 1 | 0.01% |
| OEDC0703 | 1 | 0.01% |
| OEDC0738 | 1 | 0.01% |
| OEDC1184 | 1 | 0.01% |
| OEDC1534 | 1 | 0.01% |
| OEDC1600 | 1 | 0.01% |
| OEDC0265 | 1 | 0.01% |
| OEDC0008 | 1 | 0.01% |
| OEDC1120 | 1 | 0.01% |
| OEDC0331 | 1 | 0.01% |
| OEDC0499 | 1 | 0.01% |
| OEDC0213 | 1 | 0.01% |
| OEDC0755 | 1 | 0.01% |
| OEDC1104 | 1 | 0.01% |
| OEDC0807 | 1 | 0.01% |
| OEDC0171 | 1 | 0.01% |
| OEDC1286 | 1 | 0.01% |
| OEDC0649 | 1 | 0.01% |
| OEDC1551 | 1 | 0.01% |
| OEDC1324 | 1 | 0.01% |
| OEDC0368 | 1 | 0.01% |
| OEDC1248 | 1 | 0.01% |
| OEDC1412 | 1 | 0.01% |
| OEDC0446 | 1 | 0.01% |
| OEDC1000 | 1 | 0.01% |
| OEDC0159 | 1 | 0.01% |
| OEDC0961 | 1 | 0.01% |
| OEDC1203 | 1 | 0.01% |
| OEDC1039 | 1 | 0.01% |
| OEDC0229 | 1 | 0.01% |
| OEDC0406 | 1 | 0.01% |
| OEDC0402 | 1 | 0.01% |
| OEDC0813 | 1 | 0.01% |
| OEDC0548 | 1 | 0.01% |
| OEDC1456 | 1 | 0.01% |
| OEDC1078 | 1 | 0.01% |
| OEDC0424 | 1 | 0.01% |
| OEDC0941 | 1 | 0.01% |
| OEDC0202 | 1 | 0.01% |
| OEDC0289 | 1 | 0.01% |
| OEDC1540 | 1 | 0.01% |
| OEDC1158 | 1 | 0.01% |
| OEDC0305 | 1 | 0.01% |
| OEDC0036 | 1 | 0.01% |
| OEDC0244 | 1 | 0.01% |
| OEDC0537 | 1 | 0.01% |
| OEDC1296 | 1 | 0.01% |
| OEDC1183 | 1 | 0.01% |
| OEDC0372 | 1 | 0.01% |
| OEDC1546 | 1 | 0.01% |
| OEDC0226 | 1 | 0.01% |
| OEDC1062 | 1 | 0.01% |
| OEDC1405 | 1 | 0.01% |
| OEDC0515 | 1 | 0.01% |
| OEDC0324 | 1 | 0.01% |
| OEDC0847 | 1 | 0.01% |
| OEDC1323 | 1 | 0.01% |
| OEDC0739 | 1 | 0.01% |
| OEDC0260 | 1 | 0.01% |
| OEDC1643 | 1 | 0.01% |
| OEDC0765 | 1 | 0.01% |
| OEDC0182 | 1 | 0.01% |
| OEDC1328 | 1 | 0.01% |
| OEDC1579 | 1 | 0.01% |
| OEDC0027 | 1 | 0.01% |
| OEDC0694 | 1 | 0.01% |
| OEDC0340 | 1 | 0.01% |
| OEDC1604 | 1 | 0.01% |
| OEDC1514 | 1 | 0.01% |
| OEDC0005 | 1 | 0.01% |
| OEDC0165 | 1 | 0.01% |
| OEDC0958 | 1 | 0.01% |
| OEDC0462 | 1 | 0.01% |
| OEDC0282 | 1 | 0.01% |
| OEDC0624 | 1 | 0.01% |
| OEDC0164 | 1 | 0.01% |
| OEDC0889 | 1 | 0.01% |
| OEDC0721 | 1 | 0.01% |
| OEDC1365 | 1 | 0.01% |
| OEDC0344 | 1 | 0.01% |
| OEDC0217 | 1 | 0.01% |
| OEDC0445 | 1 | 0.01% |
| OEDC1172 | 1 | 0.01% |
| OEDC1136 | 1 | 0.01% |
| OEDC1396 | 1 | 0.01% |
| OEDC0857 | 1 | 0.01% |
| OEDC1390 | 1 | 0.01% |
| OEDC0298 | 1 | 0.01% |
| OEDC1043 | 1 | 0.01% |
| OEDC0048 | 1 | 0.01% |
| OEDC0905 | 1 | 0.01% |
| OEDC0827 | 1 | 0.01% |
data['EDC_Owner'].replace({'OEDC0377':1}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
unused = data['EDC_Owner'].loc[data['EDC_Owner']!= 1]
data.replace(unused.values, 0, inplace=True)
data
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10495 | 2018-12-31 15:47:34.782 | 0 | 5000000.0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1667486.67 | 20000000.0 | 50000.0 | 4.15 | 0 |
| 10496 | 2018-12-31 15:47:34.782 | 0 | 4800000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 2530285.33 | 5000000.0 | 1000000.0 | 1.64 | 0 |
| 10497 | 2018-12-31 15:47:34.782 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1225000.00 | 10000000.0 | 100000.0 | 1.97 | 0 |
| 10498 | 2018-12-31 15:47:34.782 | 0 | 1100000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 820036.79 | 4000000.0 | 50000.0 | 3.16 | 0 |
| 10499 | 2018-12-31 22:11:05.961 | 0 | 102500.0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 571282.15 | 5000000.0 | 25000.0 | 2.36 | 0 |
10500 rows × 15 columns
data['Month'] = data['Transaction_Date'].dt.month_name()
data['Day'] = data['Transaction_Date'].dt.day_name()
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | Month | Day | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 | January | Monday |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 | January | Monday |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 | January | Monday |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 | January | Monday |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 | January | Monday |
data['Day'].replace({'Sunday': 0, 'Monday': 1, 'Tuesday':2, 'Wednesday':3, 'Thursday': 4, 'Friday': 5, 'Saturday': 6, 'Sunday': 7}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | Month | Day | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 | January | 1 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 | January | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 | January | 1 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 | January | 1 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 | January | 1 |
data['Month'].replace({'January': 0, 'February': 1, 'March':2, 'April':3, 'May': 4, 'June': 5, 'July': 6, 'August': 7,'September':8,'October':9,'November':10,'December':11}, inplace=True)
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | Month | Day | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 | 0 | 1 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 | 0 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 | 0 | 1 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 | 0 | 1 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 | 0 | 1 |
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10500 entries, 0 to 10499 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transaction_Date 10500 non-null datetime64[ns] 1 Transaction_Type 10500 non-null int64 2 Transaction_Amount 10500 non-null float64 3 Card_Type 10500 non-null int64 4 Card_Holder 10500 non-null int64 5 Channel_ID 10500 non-null int64 6 Merchant_ID 10500 non-null int64 7 City_ID 10500 non-null int64 8 EDC_Type 10500 non-null int64 9 EDC_Owner 10500 non-null int64 10 Average_Transaction_Amount 10484 non-null float64 11 Maximum_Transaction_Amount 10484 non-null float64 12 Minimum_Transaction_Amount 10484 non-null float64 13 Average_Transaction_Frequency 10484 non-null float64 14 Fraud_Status 10500 non-null int64 15 Month 10500 non-null int64 16 Day 10500 non-null int64 dtypes: datetime64[ns](1), float64(5), int64(11) memory usage: 1.4 MB
data=data.loc[data['Transaction_Amount']<data['Maximum_Transaction_Amount']]
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | Month | Day | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 | 0 | 1 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 | 0 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 | 0 | 1 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 | 0 | 1 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 | 0 | 1 |
data=data.loc[data['Transaction_Amount']>data['Minimum_Transaction_Amount']]
data.head()
| Transaction_Date | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | Fraud_Status | Month | Day | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 01:48:50.951 | 0 | 50000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 661855.03 | 11000000.0 | 24212.0 | 2.91 | 0 | 0 | 1 |
| 1 | 2018-01-01 09:08:52.666 | 0 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2171907.10 | 28555000.0 | 100000.0 | 3.12 | 1 | 0 | 1 |
| 2 | 2018-01-01 09:08:52.666 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1714437.98 | 8500000.0 | 50.0 | 3.58 | 0 | 0 | 1 |
| 3 | 2018-01-01 09:45:55.969 | 1 | 1000000.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10163708.23 | 100000000.0 | 63000.0 | 2.57 | 0 | 0 | 1 |
| 4 | 2018-01-01 23:41:59.228 | 0 | 2500000.0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 690066.65 | 3523000.0 | 26500.0 | 4.23 | 0 | 0 | 1 |
response = 'Fraud_Status'
unused_glm_vars = ['Transaction_Type','EDC_Type', 'EDC_Owner','Fraud_Status','Transaction_Type']
glm_vars = list(set(data.columns) - set(unused_glm_vars))
glm_vars
['Channel_ID', 'Day', 'Merchant_ID', 'Card_Type', 'Maximum_Transaction_Amount', 'Average_Transaction_Frequency', 'City_ID', 'Transaction_Amount', 'Minimum_Transaction_Amount', 'Month', 'Average_Transaction_Amount', 'Transaction_Date', 'Card_Holder']
def target_boxplot(data, variable, target='Target', plotsize=(8, 8), colors=['green', 'red'], labels=['No', 'Yes'], limits=None):
fig, ax = plt.subplots(figsize=plotsize)
bp = sns.boxplot(x=target, y=variable, data=data, showmeans=True, meanline=True,
palette=colors, width=0.3,
flierprops=dict(marker='o', markerfacecolor='red', markeredgecolor='white', alpha=0.5),
meanprops=dict(linestyle='-', linewidth=2, color='orange'),
medianprops=dict(linestyle='-', linewidth=2, color='white'),
boxprops=dict(linewidth=0, alpha=0.7))
bp.set(xticklabels=labels, ylim=limits)
plt.show()
def bar_plot(pivot_table, color=['green', 'red'], size=(15,15), d=0.05):
ax = pivot_table.plot(kind='barh', figsize=size, fontsize=12, color=color, alpha=0.7)
for val in ax.patches:
width = val.get_width()
height = val.get_height()
x, y = val.get_xy()
ax.annotate(f'{height:.0%}', (x + height/2, y + width*1.02), ha='center')
ax.set_ylabel('')
ax.set_xticks([])
plt.show()
colors_list = ['#5cb85c','#5bc0de','#d9534f']
# Normalize result
result_pct = result.div(result.sum(1), axis=0)
ax = result_pct.plot(kind='bar',figsize=(15,4),width = 0.8,color = colors_list,edgecolor=None)
plt.legend(labels=result.columns,fontsize= 14)
plt.title("Percentage of Respondents' Interest in Data Science Areas",fontsize= 16)
plt.xticks(fontsize=14)
for spine in plt.gca().spines.values():
spine.set_visible(False)
plt.yticks([])
# Add this loop to add the annotations
for var in ['Transaction_Amount', 'Average_Transaction_Frequency', 'Maximum_Transaction_Amount', 'Average_Transaction_Amount','Minimum_Transaction_Amount']:
plt.figure()
target_boxplot(data, var, target=response, labels=['Not Fraud', 'Fraud'])
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
pvt = pd.crosstab(index=data['Month'], columns=data['Fraud_Status'])
pvt
| Fraud_Status | 0 | 1 |
|---|---|---|
| Month | ||
| 0 | 126 | 12 |
| 1 | 52 | 3 |
| 2 | 122 | 17 |
| 3 | 600 | 50 |
| 4 | 1074 | 72 |
| 5 | 1280 | 84 |
| 6 | 1219 | 78 |
| 7 | 1176 | 79 |
| 8 | 1214 | 68 |
| 9 | 1180 | 57 |
| 10 | 797 | 44 |
| 11 | 308 | 21 |
bar_plot(pvt, d=0.05)
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) /Applications/anaconda3/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj) 339 pass 340 else: --> 341 return printer(obj) 342 # Finally look for special method names 343 method = get_real_method(obj, self.print_method) /Applications/anaconda3/lib/python3.8/site-packages/IPython/core/pylabtools.py in <lambda>(fig) 246 247 if 'png' in formats: --> 248 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 249 if 'retina' in formats or 'png2x' in formats: 250 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) /Applications/anaconda3/lib/python3.8/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 130 FigureCanvasBase(fig) 131 --> 132 fig.canvas.print_figure(bytes_io, **kw) 133 data = bytes_io.getvalue() 134 if fmt == 'svg': /Applications/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs) 2208 2209 try: -> 2210 result = print_method( 2211 filename, 2212 dpi=dpi, /Applications/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs) 1637 kwargs.pop(arg) 1638 -> 1639 return func(*args, **kwargs) 1640 1641 return wrapper /Applications/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args) 507 *metadata*, including the default 'Software' key. 508 """ --> 509 FigureCanvasAgg.draw(self) 510 mpl.image.imsave( 511 filename_or_obj, self.buffer_rgba(), format="png", origin="upper", /Applications/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self) 400 def draw(self): 401 # docstring inherited --> 402 self.renderer = self.get_renderer(cleared=True) 403 # Acquire a lock on the shared font cache. 404 with RendererAgg.lock, \ /Applications/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in get_renderer(self, cleared) 416 and getattr(self, "_lastKey", None) == key) 417 if not reuse_renderer: --> 418 self.renderer = RendererAgg(w, h, self.figure.dpi) 419 self._lastKey = key 420 elif cleared: /Applications/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi) 94 self.width = width 95 self.height = height ---> 96 self._renderer = _RendererAgg(int(width), int(height), dpi) 97 self._filter_renderers = [] 98 ValueError: Image size of 874x89094 pixels is too large. It must be less than 2^16 in each direction.
<Figure size 1080x1080 with 1 Axes>
data['Transaction_Type'].value_counts()
0 7417 1 2316 Name: Transaction_Type, dtype: int64
cardtype=data[["Card_Type", 'Fraud_Status' ]].groupby(["Card_Type"], as_index=False).mean().sort_values(by='Fraud_Status', ascending=False)
fcardtype=cardtype.head()
fcardtype
| Card_Type | Fraud_Status | |
|---|---|---|
| 0 | 0 | 0.064023 |
| 1 | 1 | 0.045117 |
sns.set_theme(style="whitegrid")
ax = sns.barplot(x="Card_Type", y="Fraud_Status", data=fcardtype)
cardholder=data[["Card_Holder", 'Fraud_Status' ]].groupby(["Card_Holder"], as_index=False).mean().sort_values(by='Fraud_Status', ascending=False)
cardholder
| Card_Holder | Fraud_Status | |
|---|---|---|
| 0 | 0 | 0.381538 |
| 1 | 1 | 0.037102 |
sns.set_theme(style="whitegrid")
ax = sns.barplot(x="Card_Holder", y="Fraud_Status", data=cardholder)
data.groupby('Card_Type')['Fraud_Status'].value_counts().unstack().stack(dropna=False).reset_index(name="Count").set_index(['Card_Type', 'Fraud_Status'])
| Count | ||
|---|---|---|
| Card_Type | Fraud_Status | |
| 0 | 0 | 7222 |
| 1 | 494 | |
| 1 | 0 | 1926 |
| 1 | 91 |
cardholder=data.groupby('Card_Holder')['Fraud_Status'].value_counts().unstack().stack(dropna=False).reset_index(name="Count").set_index(['Card_Holder', 'Fraud_Status'])
cardholder
| Count | ||
|---|---|---|
| Card_Holder | Fraud_Status | |
| 0 | 0 | 402 |
| 1 | 248 | |
| 1 | 0 | 8746 |
| 1 | 337 |
cardholder.unstack().plot(kind='bar', stacked=True)
<AxesSubplot:xlabel='Card_Holder'>
Nearly half the transaction of card holder 0 is fraud. We assume that card holder must be a significant predictor to fraud. Let's see if our assumption is right.
def correlation_plot(df, plotsize=(15,15), corr_type='spearman', number_format='.1%', fontsize=11):
corrs = df.corr(method=corr_type)
triangle = np.triu(corrs)
fig, ax = plt.subplots(figsize=plotsize)
ax = sns.heatmap(corrs, mask=triangle, annot=True, annot_kws={'size': fontsize}, fmt=number_format, cmap='RdYlGn_r',
cbar_kws={'shrink': 0.8}, vmin=-1, center=0, vmax=1, square=True, linewidths=0.1, linecolor='white')
plt.show()
correlation_plot(data, fontsize=14)
It's true that Card Holder holds the most correlated comparing to the other variables.
import ppscore as pps
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from itertools import combinations
def pps_correlations(df, variables, corr_type='pearson', limit=0.8, style=True):
vars1, vars2 = [], []
corrs, pvals = [], []
ppscores1, ppscores2 = [], []
for var1, var2 in combinations(variables, 2):
if corr_type == 'pearson':
corr, pval = pearsonr(df[var1], df[var2])
elif corr_type == 'spearman':
corr, pval = spearmanr(df[var1], df[var2])
else:
print('Unknown correlation method. Please input either \'pearson\' or \'spearman\' in argument corr_type.')
break
vars1.append(var1)
vars2.append(var2)
corrs.append(corr)
pvals.append(pval)
ppcorr = pd.DataFrame({'Variable 1': vars1, 'Variable 2': vars2, 'Correlation': corrs, 'P-Value': pvals})
cols = list(ppcorr.columns)
ppcorr['Absolute'] = ppcorr['Correlation'].abs()
ppcorr.sort_values(by='Absolute', ascending=False, inplace=True)
ppcorr.reset_index(drop=True, inplace=True)
ppcorr = ppcorr.loc[ppcorr['Absolute'] >= limit, cols].copy()
vars1 = ppcorr['Variable 1'].tolist()
vars2 = ppcorr['Variable 2'].tolist()
for var1, var2 in zip(vars1, vars2):
ppscores1.append(pps.score(df, var1, var2)['ppscore'])
ppscores2.append(pps.score(df, var2, var1)['ppscore'])
ppcorr['PPS 1'] = ppscores1
ppcorr['PPS 2'] = ppscores2
cols.insert(2, 'PPS 2')
cols.insert(2, 'PPS 1')
ppcorr = ppcorr[cols]
ppcorr.index += 1
if style:
formats = {'PPS 1': '{:.2%}', 'PPS 2': '{:.2%}', 'Correlation': '{:.2%}', 'P-Value': '{:.2%}'}
ppcorr = ppcorr.style.format(formats)
return ppcorr
from statsmodels.tools.tools import add_constant
from statsmodels.stats.outliers_influence import variance_inflation_factor
def vif(data, features, style=True):
# Add constant to the GLM variables
X = add_constant(data[features])
vif_values = [variance_inflation_factor(X.values, i) for i in range(1, X.shape[1])]
vif_df = pd.DataFrame({'Variable': features, 'VIF': vif_values})
vif_df = vif_df.sort_values('VIF', ascending=False).reset_index(drop=True)
vif_df.index += 1
if style:
vif_df = vif_df.style.format({'VIF': '{:,.2f}'})
return vif_df
vif(data, glm_vars)
/Applications/anaconda3/lib/python3.8/site-packages/statsmodels/stats/outliers_influence.py:193: RuntimeWarning: divide by zero encountered in double_scalars vif = 1. / (1. - r_squared_i)
| Variable | VIF | |
|---|---|---|
| 1 | Channel_ID | inf |
| 2 | Card_Holder | inf |
| 3 | Merchant_ID | inf |
| 4 | Average_Transaction_Amount | 2.41 |
| 5 | Maximum_Transaction_Amount | 2.33 |
| 6 | Card_Type | 1.29 |
| 7 | Transaction_Amount | 1.28 |
| 8 | Average_Transaction_Frequency | 1.12 |
| 9 | Minimum_Transaction_Amount | 1.11 |
| 10 | City_ID | 1.02 |
pps_correlations(data, glm_vars, limit=0)
| Variable 1 | Variable 2 | PPS 1 | PPS 2 | Correlation | P-Value | |
|---|---|---|---|---|---|---|
| 1 | Channel_ID | Merchant_ID | 36.34% | 41.19% | 78.93% | 0.00% |
| 2 | Maximum_Transaction_Amount | Average_Transaction_Amount | 45.16% | 39.10% | 69.23% | 0.00% |
| 3 | Channel_ID | Card_Holder | 0.00% | 0.00% | 52.57% | 0.00% |
| 4 | Card_Type | Maximum_Transaction_Amount | 0.00% | 10.69% | 46.85% | 0.00% |
| 5 | Transaction_Amount | Average_Transaction_Amount | 5.40% | 0.00% | 45.53% | 0.00% |
| 6 | Card_Type | Average_Transaction_Amount | 0.52% | 66.03% | 32.88% | 0.00% |
| 7 | Average_Transaction_Frequency | Maximum_Transaction_Amount | 13.26% | 3.76% | 30.74% | 0.00% |
| 8 | Transaction_Amount | Maximum_Transaction_Amount | 0.00% | 0.00% | 27.89% | 0.00% |
| 9 | Minimum_Transaction_Amount | Average_Transaction_Amount | 14.73% | 0.00% | 24.82% | 0.00% |
| 10 | Average_Transaction_Frequency | Average_Transaction_Amount | 9.95% | 0.00% | 21.39% | 0.00% |
| 11 | Average_Transaction_Frequency | Card_Type | 0.00% | 0.00% | 20.10% | 0.00% |
| 12 | Transaction_Amount | Card_Type | 0.00% | 0.00% | 15.43% | 0.00% |
| 13 | Transaction_Amount | Minimum_Transaction_Amount | 0.00% | 0.00% | 14.00% | 0.00% |
| 14 | City_ID | Card_Holder | 0.00% | 0.00% | -11.88% | 0.00% |
| 15 | Card_Holder | Merchant_ID | 0.00% | 0.00% | -10.74% | 0.00% |
| 16 | Transaction_Amount | Merchant_ID | 10.25% | 0.00% | 10.56% | 0.00% |
| 17 | Transaction_Amount | Average_Transaction_Frequency | 0.00% | 0.00% | 10.54% | 0.00% |
| 18 | Transaction_Amount | Channel_ID | 0.00% | 0.00% | 9.81% | 0.00% |
| 19 | Minimum_Transaction_Amount | Merchant_ID | 0.00% | 0.00% | 6.82% | 0.00% |
| 20 | Average_Transaction_Amount | Merchant_ID | 0.00% | 0.00% | 6.65% | 0.00% |
| 21 | Maximum_Transaction_Amount | Minimum_Transaction_Amount | 0.00% | 2.57% | 5.65% | 0.00% |
| 22 | City_ID | Channel_ID | 0.00% | 0.00% | -5.63% | 0.00% |
| 23 | Channel_ID | Average_Transaction_Amount | 0.00% | 0.00% | 5.31% | 0.00% |
| 24 | Average_Transaction_Frequency | Minimum_Transaction_Amount | 0.00% | 0.00% | -4.56% | 0.00% |
| 25 | Card_Holder | Minimum_Transaction_Amount | 0.00% | 0.00% | -4.12% | 0.00% |
| 26 | Card_Type | Minimum_Transaction_Amount | 0.00% | 0.00% | 3.58% | 0.04% |
| 27 | Channel_ID | Minimum_Transaction_Amount | 0.00% | 0.00% | 3.29% | 0.12% |
| 28 | Channel_ID | Maximum_Transaction_Amount | 0.00% | 0.00% | 2.55% | 1.20% |
| 29 | Average_Transaction_Frequency | Channel_ID | 0.00% | 0.00% | 2.54% | 1.23% |
| 30 | Average_Transaction_Frequency | Merchant_ID | 0.00% | 0.00% | 2.48% | 1.43% |
| 31 | Maximum_Transaction_Amount | Merchant_ID | 0.00% | 0.00% | 2.37% | 1.95% |
| 32 | City_ID | Minimum_Transaction_Amount | 0.00% | 0.00% | 2.23% | 2.80% |
| 33 | City_ID | Merchant_ID | 0.00% | 0.00% | 2.00% | 4.84% |
| 34 | City_ID | Card_Type | 0.00% | 0.00% | 1.90% | 6.05% |
| 35 | Transaction_Amount | City_ID | 0.00% | 0.00% | 1.50% | 13.81% |
| 36 | Transaction_Amount | Card_Holder | 0.00% | 0.00% | 1.25% | 21.86% |
| 37 | Card_Holder | Maximum_Transaction_Amount | 0.00% | 0.00% | 0.84% | 40.63% |
| 38 | Average_Transaction_Frequency | Card_Holder | 0.00% | 0.00% | 0.67% | 50.88% |
| 39 | Card_Type | Channel_ID | 0.00% | 0.00% | 0.62% | 54.10% |
| 40 | Card_Holder | Average_Transaction_Amount | 0.00% | 0.00% | -0.61% | 54.67% |
| 41 | Card_Type | Merchant_ID | 0.00% | 0.00% | 0.60% | 55.43% |
| 42 | City_ID | Average_Transaction_Amount | 0.00% | 0.00% | 0.41% | 68.59% |
| 43 | Average_Transaction_Frequency | City_ID | 0.00% | 0.00% | -0.39% | 70.03% |
| 44 | City_ID | Maximum_Transaction_Amount | 0.00% | 0.00% | -0.32% | 75.55% |
| 45 | Card_Type | Card_Holder | 0.00% | 0.00% | 0.17% | 86.47% |
import h2o
h2o.init()
from h2o.automl import H2OAutoML
Checking whether there is an H2O instance running at http://localhost:54321 . connected.
| H2O_cluster_uptime: | 17 days 11 hours 3 mins |
| H2O_cluster_timezone: | Asia/Jakarta |
| H2O_data_parsing_timezone: | UTC |
| H2O_cluster_version: | 3.32.1.3 |
| H2O_cluster_version_age: | 1 month and 26 days |
| H2O_cluster_name: | H2O_from_python_charissa_9lw8gd |
| H2O_cluster_total_nodes: | 1 |
| H2O_cluster_free_memory: | 1.159 Gb |
| H2O_cluster_total_cores: | 8 |
| H2O_cluster_allowed_cores: | 8 |
| H2O_cluster_status: | locked, healthy |
| H2O_connection_url: | http://localhost:54321 |
| H2O_connection_proxy: | {"http": null, "https": null} |
| H2O_internal_security: | False |
| H2O_API_Extensions: | Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4 |
| Python_version: | 3.8.8 final |
def timer(start_time, header='\nProgram Schedule'):
end_time = pd.to_datetime('now') + pd.Timedelta('07:00:00')
print(header)
print(start_time.strftime('Start : %Y-%m-%d %I:%M:%S %p'))
print(end_time.strftime('Finish : %Y-%m-%d %I:%M:%S %p'))
run_time = str(end_time - start_time)
idx1 = run_time.find(' ', 3) + 1
idx2 = run_time.find('.')
print('Runtime :', run_time[idx1 : idx2], '\n')
from sklearn.model_selection import train_test_split
start_time = pd.to_datetime('now') + pd.Timedelta('07:00:00')
trains=[]
tests=[]
for i in range(0, 30):
train, test = train_test_split(data, test_size=0.2, stratify=data['Fraud_Status'])
train = h2o.H2OFrame(train)
test = h2o.H2OFrame(test)
train['Fraud_Status'] = train['Fraud_Status'].asfactor()
test['Fraud_Status'] = test['Fraud_Status'].asfactor()
train['Transaction_Type'] = train['Transaction_Type'].asfactor()
test['Transaction_Type'] = test['Transaction_Type'].asfactor()
train['Card_Type'] = train['Card_Type'].asfactor()
test['Card_Type'] = test['Card_Type'].asfactor()
trains.append(train)
tests.append(test)
timer(start_time, header='\nConverting DataFrame to H2O Frame')
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start_time = pd.to_datetime('now') + pd.Timedelta('07:00:00')
models=[]
for train, test in zip(trains, tests):
GLM = H2OAutoML(max_runtime_secs=300, nfolds=30, include_algos=['GLM'])
GLM.train(x=glm_vars, y='Fraud_Status', training_frame=train, validation_frame=test)
models.append(GLM.leader)
timer(start_time, header='\nModeling')
AutoML progress: | 20:11:08.884: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:11:21.258: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:11:31.783: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:11:40.639: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:11:51.835: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:12:03.146: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:12:13.541: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:12:25.781: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:12:35.780: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:12:49.225: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:12:58.494: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:13:10.62: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:13:19.401: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:13:31.994: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:13:42.919: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:13:54.756: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:14:06.719: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:14:19.528: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:14:30.869: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:14:43.292: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:14:54.424: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:15:05.296: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:15:17.961: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:15:33.585: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:15:48.315: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:16:01.238: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:16:11.651: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:16:23.833: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:16:34.61: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% AutoML progress: | 20:16:47.766: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. ████████████████████████████████████████████████████████| 100% Modeling Start : 2021-07-15 08:11:08 PM Finish : 2021-07-15 08:16:59 PM Runtime : 00:05:50
models
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201108 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.007046 ) | nlambda = 30, lambda.max = 8.9414, lambda.min = 0.007046, lambda.1... | 14 | 14 | 32 | automl_training_py_3_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04752236156796884 RMSE: 0.21799624209597934 LogLoss: 0.1840262580204793 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938643 Residual deviance: 2865.6568898949035 AIC: 2895.6568898949035 AUC: 0.7794022408158785 AUCPR: 0.31251018779415657 Gini: 0.558804481631757 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.29512062976335124:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7036.0 | 282.0 | 0.0385 | (282.0/7318.0) |
| 1 | 1 | 271.0 | 197.0 | 0.5791 | (271.0/468.0) |
| 2 | Total | 7307.0 | 479.0 | 0.071 | (553.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.295121 | 0.416051 | 139.0 |
| 1 | max f2 | 0.063268 | 0.450928 | 227.0 |
| 2 | max f0point5 | 0.364045 | 0.432135 | 97.0 |
| 3 | max accuracy | 0.447983 | 0.941947 | 40.0 |
| 4 | max precision | 0.838133 | 1.000000 | 0.0 |
| 5 | max recall | 0.017840 | 1.000000 | 380.0 |
| 6 | max specificity | 0.838133 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.295121 | 0.378273 | 139.0 |
| 8 | max min_per_class_accuracy | 0.040735 | 0.698718 | 284.0 |
| 9 | max mean_per_class_accuracy | 0.052311 | 0.724485 | 246.0 |
| 10 | max tns | 0.838133 | 7318.000000 | 0.0 |
| 11 | max fns | 0.838133 | 467.000000 | 0.0 |
| 12 | max fps | 0.001284 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017840 | 468.000000 | 380.0 |
| 14 | max tnr | 0.838133 | 1.000000 | 0.0 |
| 15 | max fnr | 0.838133 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001284 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017840 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.438414 | 9.598126 | 9.598126 | 0.576923 | 0.508515 | 0.576923 | 0.508515 | 0.096154 | 0.096154 | 859.812623 | 859.812623 | 0.091644 |
| 1 | 2 | 0.020036 | 0.408233 | 7.465209 | 8.531668 | 0.448718 | 0.421909 | 0.512821 | 0.465212 | 0.074786 | 0.170940 | 646.520929 | 753.166776 | 0.160555 |
| 2 | 3 | 0.030054 | 0.381625 | 7.465209 | 8.176182 | 0.448718 | 0.394570 | 0.491453 | 0.441665 | 0.074786 | 0.245726 | 646.520929 | 717.618161 | 0.229465 |
| 3 | 4 | 0.040072 | 0.364233 | 6.825334 | 7.838470 | 0.410256 | 0.373182 | 0.471154 | 0.424544 | 0.068376 | 0.314103 | 582.533421 | 683.846976 | 0.291555 |
| 4 | 5 | 0.050090 | 0.337932 | 4.905709 | 7.251918 | 0.294872 | 0.351083 | 0.435897 | 0.409852 | 0.049145 | 0.363248 | 390.570896 | 625.191760 | 0.333185 |
| 5 | 6 | 0.100051 | 0.062547 | 2.950992 | 5.104215 | 0.177378 | 0.168332 | 0.306804 | 0.289247 | 0.147436 | 0.510684 | 195.099202 | 410.421535 | 0.436893 |
| 6 | 7 | 0.150013 | 0.049639 | 1.154736 | 3.788849 | 0.069409 | 0.054386 | 0.227740 | 0.211027 | 0.057692 | 0.568376 | 15.473601 | 278.884937 | 0.445118 |
| 7 | 8 | 0.200103 | 0.045639 | 0.981142 | 3.086021 | 0.058974 | 0.047469 | 0.185494 | 0.170085 | 0.049145 | 0.617521 | -1.885821 | 208.602142 | 0.444113 |
| 8 | 9 | 0.300026 | 0.041304 | 0.705672 | 2.293251 | 0.042416 | 0.043220 | 0.137842 | 0.127833 | 0.070513 | 0.688034 | -29.432799 | 129.325094 | 0.412822 |
| 9 | 10 | 0.400077 | 0.038321 | 0.405774 | 1.821230 | 0.024390 | 0.039729 | 0.109470 | 0.105800 | 0.040598 | 0.728632 | -59.422556 | 82.123033 | 0.349567 |
| 10 | 11 | 0.500000 | 0.035868 | 0.769824 | 1.611111 | 0.046272 | 0.037121 | 0.096840 | 0.092075 | 0.076923 | 0.805556 | -23.017599 | 61.111111 | 0.325096 |
| 11 | 12 | 0.600051 | 0.033556 | 0.619340 | 1.445745 | 0.037227 | 0.034712 | 0.086901 | 0.082510 | 0.061966 | 0.867521 | -38.066006 | 44.574516 | 0.284575 |
| 12 | 13 | 0.699974 | 0.030982 | 0.406296 | 1.297361 | 0.024422 | 0.032316 | 0.077982 | 0.075345 | 0.040598 | 0.908120 | -59.370400 | 29.736141 | 0.221457 |
| 13 | 14 | 0.800026 | 0.027909 | 0.341705 | 1.177847 | 0.020539 | 0.029519 | 0.070798 | 0.069614 | 0.034188 | 0.942308 | -65.829521 | 17.784680 | 0.151381 |
| 14 | 15 | 0.899949 | 0.023118 | 0.320760 | 1.082683 | 0.019280 | 0.025774 | 0.065078 | 0.064746 | 0.032051 | 0.974359 | -67.924000 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.001099 | 0.256279 | 1.000000 | 0.015404 | 0.018387 | 0.060108 | 0.060108 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.05027232283187711 RMSE: 0.22421490323320864 LogLoss: 0.19448948179990513 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311489 Residual deviance: 757.3420421288304 AIC: 787.3420421288304 AUC: 0.7457078137406006 AUCPR: 0.22155303320166622 Gini: 0.49141562748120116 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.10879492515232687:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1747.0 | 83.0 | 0.0454 | (83.0/1830.0) |
| 1 | 1 | 69.0 | 48.0 | 0.5897 | (69.0/117.0) |
| 2 | Total | 1816.0 | 131.0 | 0.0781 | (152.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.108795 | 0.387097 | 114.0 |
| 1 | max f2 | 0.108795 | 0.400668 | 114.0 |
| 2 | max f0point5 | 0.260051 | 0.381282 | 98.0 |
| 3 | max accuracy | 0.575565 | 0.940421 | 2.0 |
| 4 | max precision | 0.575565 | 0.666667 | 2.0 |
| 5 | max recall | 0.019202 | 1.000000 | 369.0 |
| 6 | max specificity | 0.811500 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.108795 | 0.346179 | 114.0 |
| 8 | max min_per_class_accuracy | 0.040037 | 0.662842 | 237.0 |
| 9 | max mean_per_class_accuracy | 0.057428 | 0.685785 | 156.0 |
| 10 | max tns | 0.811500 | 1829.000000 | 0.0 |
| 11 | max fns | 0.811500 | 117.000000 | 0.0 |
| 12 | max fps | 0.001316 | 1830.000000 | 399.0 |
| 13 | max tps | 0.019202 | 117.000000 | 369.0 |
| 14 | max tnr | 0.811500 | 0.999454 | 0.0 |
| 15 | max fnr | 0.811500 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001316 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019202 | 1.000000 | 369.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.79 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.430373 | 4.992308 | 4.992308 | 0.300000 | 0.502404 | 0.300000 | 0.502404 | 0.051282 | 0.051282 | 399.230769 | 399.230769 | 0.043632 |
| 1 | 2 | 0.020031 | 0.395597 | 7.882591 | 6.400394 | 0.473684 | 0.411902 | 0.384615 | 0.458314 | 0.076923 | 0.128205 | 688.259109 | 540.039448 | 0.115090 |
| 2 | 3 | 0.030303 | 0.379552 | 5.824359 | 6.205128 | 0.350000 | 0.387677 | 0.372881 | 0.434369 | 0.059829 | 0.188034 | 482.435897 | 520.512821 | 0.167816 |
| 3 | 4 | 0.040062 | 0.355861 | 6.130904 | 6.187048 | 0.368421 | 0.366353 | 0.371795 | 0.417801 | 0.059829 | 0.247863 | 513.090418 | 518.704799 | 0.221087 |
| 4 | 5 | 0.050334 | 0.311663 | 7.488462 | 6.452643 | 0.450000 | 0.338321 | 0.387755 | 0.401581 | 0.076923 | 0.324786 | 648.846154 | 545.264260 | 0.291999 |
| 5 | 6 | 0.100154 | 0.057917 | 2.401798 | 4.437607 | 0.144330 | 0.130577 | 0.266667 | 0.266774 | 0.119658 | 0.444444 | 140.179752 | 343.760684 | 0.366302 |
| 6 | 7 | 0.149974 | 0.048796 | 0.343114 | 3.077450 | 0.020619 | 0.053267 | 0.184932 | 0.195848 | 0.017094 | 0.461538 | -65.688607 | 207.744995 | 0.331484 |
| 7 | 8 | 0.200308 | 0.045096 | 0.849032 | 2.517488 | 0.051020 | 0.046789 | 0.151282 | 0.158392 | 0.042735 | 0.504274 | -15.096808 | 151.748849 | 0.323399 |
| 8 | 9 | 0.299949 | 0.041602 | 1.029342 | 2.023138 | 0.061856 | 0.043105 | 0.121575 | 0.120095 | 0.102564 | 0.606838 | 2.934179 | 102.313839 | 0.326510 |
| 9 | 10 | 0.400103 | 0.038567 | 1.280079 | 1.837135 | 0.076923 | 0.040124 | 0.110398 | 0.100077 | 0.128205 | 0.735043 | 28.007890 | 83.713505 | 0.356354 |
| 10 | 11 | 0.500257 | 0.036109 | 0.512032 | 1.571842 | 0.030769 | 0.037345 | 0.094456 | 0.087517 | 0.051282 | 0.786325 | -48.796844 | 57.184226 | 0.304358 |
| 11 | 12 | 0.599897 | 0.033822 | 0.686228 | 1.424745 | 0.041237 | 0.034981 | 0.085616 | 0.078791 | 0.068376 | 0.854701 | -31.377214 | 42.474535 | 0.271094 |
| 12 | 13 | 0.700051 | 0.031282 | 0.768047 | 1.330794 | 0.046154 | 0.032587 | 0.079971 | 0.072181 | 0.076923 | 0.931624 | -23.195266 | 33.079369 | 0.246378 |
| 13 | 14 | 0.799692 | 0.027963 | 0.171557 | 1.186354 | 0.010309 | 0.029675 | 0.071291 | 0.066885 | 0.017094 | 0.948718 | -82.844303 | 18.635443 | 0.158554 |
| 14 | 15 | 0.899846 | 0.022994 | 0.256016 | 1.082806 | 0.015385 | 0.025738 | 0.065068 | 0.062305 | 0.025641 | 0.974359 | -74.398422 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.001316 | 0.256016 | 1.000000 | 0.015385 | 0.018793 | 0.060092 | 0.057947 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.047653128252676175 RMSE: 0.21829596481079575 LogLoss: 0.18568864879336736 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.0611592595515 Residual deviance: 2891.5436390103164 AIC: 2921.5436390103164 AUC: 0.7634122220587101 AUCPR: 0.3034137782994518 Gini: 0.5268244441174201 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.11952626883590241:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6992.0 | 326.0 | 0.0445 | (326.0/7318.0) |
| 1 | 1 | 258.0 | 210.0 | 0.5513 | (258.0/468.0) |
| 2 | Total | 7250.0 | 536.0 | 0.075 | (584.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.119526 | 0.418327 | 190.0 |
| 1 | max f2 | 0.062429 | 0.447217 | 227.0 |
| 2 | max f0point5 | 0.371643 | 0.433468 | 91.0 |
| 3 | max accuracy | 0.450977 | 0.941819 | 39.0 |
| 4 | max precision | 0.844948 | 1.000000 | 0.0 |
| 5 | max recall | 0.015479 | 1.000000 | 386.0 |
| 6 | max specificity | 0.844948 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.282622 | 0.380178 | 144.0 |
| 8 | max min_per_class_accuracy | 0.041906 | 0.692308 | 279.0 |
| 9 | max mean_per_class_accuracy | 0.062429 | 0.714769 | 227.0 |
| 10 | max tns | 0.844948 | 7318.000000 | 0.0 |
| 11 | max fns | 0.844948 | 467.000000 | 0.0 |
| 12 | max fps | 0.001381 | 7318.000000 | 399.0 |
| 13 | max tps | 0.015479 | 468.000000 | 386.0 |
| 14 | max tnr | 0.844948 | 1.000000 | 0.0 |
| 15 | max fnr | 0.844948 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001381 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015479 | 1.000000 | 386.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.99 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.435563 | 9.171543 | 9.171543 | 0.551282 | 0.505493 | 0.551282 | 0.505493 | 0.091880 | 0.091880 | 817.154284 | 817.154284 | 0.087098 |
| 1 | 2 | 0.020036 | 0.398264 | 7.678501 | 8.425022 | 0.461538 | 0.414682 | 0.506410 | 0.460088 | 0.076923 | 0.168803 | 667.850099 | 742.502192 | 0.158281 |
| 2 | 3 | 0.030054 | 0.375837 | 8.105084 | 8.318376 | 0.487179 | 0.386777 | 0.500000 | 0.435651 | 0.081197 | 0.250000 | 710.508437 | 731.837607 | 0.234012 |
| 3 | 4 | 0.040072 | 0.355259 | 5.758876 | 7.678501 | 0.346154 | 0.366244 | 0.461538 | 0.418299 | 0.057692 | 0.307692 | 475.887574 | 667.850099 | 0.284735 |
| 4 | 5 | 0.050090 | 0.322117 | 5.758876 | 7.294576 | 0.346154 | 0.339368 | 0.438462 | 0.402513 | 0.057692 | 0.365385 | 475.887574 | 629.457594 | 0.335458 |
| 5 | 6 | 0.129335 | 0.061129 | 1.887476 | 3.981586 | 0.113452 | 0.111129 | 0.239325 | 0.223979 | 0.149573 | 0.514957 | 88.747593 | 298.158616 | 0.410284 |
| 6 | 7 | 0.162856 | 0.060856 | 0.637423 | 3.293237 | 0.038314 | 0.060856 | 0.197950 | 0.190402 | 0.021368 | 0.536325 | -36.257655 | 229.323721 | 0.397352 |
| 7 | 8 | 0.200103 | 0.050559 | 1.204730 | 2.904491 | 0.072414 | 0.054781 | 0.174583 | 0.165158 | 0.044872 | 0.581197 | 20.473033 | 190.449075 | 0.405466 |
| 8 | 9 | 0.300026 | 0.043055 | 0.855360 | 2.222032 | 0.051414 | 0.046095 | 0.133562 | 0.125505 | 0.085470 | 0.666667 | -14.463999 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.039443 | 0.576627 | 1.810549 | 0.034660 | 0.041168 | 0.108828 | 0.104414 | 0.057692 | 0.724359 | -42.337316 | 81.054863 | 0.345020 |
| 10 | 11 | 0.500000 | 0.036764 | 0.555984 | 1.559829 | 0.033419 | 0.038073 | 0.093758 | 0.091156 | 0.055556 | 0.779915 | -44.401600 | 55.982906 | 0.297816 |
| 11 | 12 | 0.600051 | 0.034249 | 0.619340 | 1.403014 | 0.037227 | 0.035493 | 0.084332 | 0.081875 | 0.061966 | 0.841880 | -38.066006 | 40.301377 | 0.257294 |
| 12 | 13 | 0.699974 | 0.031651 | 0.598752 | 1.288204 | 0.035990 | 0.032966 | 0.077431 | 0.074893 | 0.059829 | 0.901709 | -40.124800 | 28.820356 | 0.214636 |
| 13 | 14 | 0.800026 | 0.028524 | 0.277635 | 1.161822 | 0.016688 | 0.030125 | 0.069835 | 0.069294 | 0.027778 | 0.929487 | -72.236486 | 16.182167 | 0.137741 |
| 14 | 15 | 0.899949 | 0.023577 | 0.363528 | 1.073186 | 0.021851 | 0.026329 | 0.064507 | 0.064524 | 0.036325 | 0.965812 | -63.647200 | 7.318567 | 0.070075 |
| 15 | 16 | 1.000000 | 0.000854 | 0.341705 | 1.000000 | 0.020539 | 0.018776 | 0.060108 | 0.059947 | 0.034188 | 1.000000 | -65.829521 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9319444 | 0.02676247 | 0.9346154 | 0.9461538 | 0.95 | 0.8346154 | 0.91923076 | 0.9269231 | 0.91923076 | 0.93846154 | 0.89615387 | 0.9461538 | 0.9115385 | 0.9346154 | 0.93846154 | 0.96153843 | 0.9115385 | 0.9230769 | 0.94208497 | 0.9150579 | 0.9266409 | 0.96525097 | 0.96138996 | 0.9150579 | 0.93822396 | 0.95752895 | 0.9227799 | 0.93822396 | 0.9111969 | 0.976834 | 0.93050194 | 0.96525097 |
| 1 | auc | 0.7699643 | 0.08766349 | 0.77792007 | 0.7270748 | 0.574 | 0.7502083 | 0.80246913 | 0.8624154 | 0.68395835 | 0.66071427 | 0.8169935 | 0.8262167 | 0.5684 | 0.79297173 | 0.55188245 | 0.8780952 | 0.75 | 0.79602534 | 0.83577406 | 0.8 | 0.7889121 | 0.8765432 | 0.74890554 | 0.7942623 | 0.86938775 | 0.88742965 | 0.7528815 | 0.8031381 | 0.7568306 | 0.7549801 | 0.8197017 | 0.79083663 |
| 2 | err | 0.06805564 | 0.02676247 | 0.06538462 | 0.053846154 | 0.05 | 0.16538462 | 0.08076923 | 0.073076926 | 0.08076923 | 0.06153846 | 0.103846155 | 0.053846154 | 0.08846154 | 0.06538462 | 0.06153846 | 0.03846154 | 0.08846154 | 0.07692308 | 0.057915058 | 0.08494209 | 0.07335907 | 0.034749035 | 0.038610037 | 0.08494209 | 0.06177606 | 0.042471044 | 0.077220075 | 0.06177606 | 0.08880309 | 0.023166023 | 0.06949807 | 0.034749035 |
| 3 | err_count | 17.666666 | 6.959654 | 17.0 | 14.0 | 13.0 | 43.0 | 21.0 | 19.0 | 21.0 | 16.0 | 27.0 | 14.0 | 23.0 | 17.0 | 16.0 | 10.0 | 23.0 | 20.0 | 15.0 | 22.0 | 19.0 | 9.0 | 10.0 | 22.0 | 16.0 | 11.0 | 20.0 | 16.0 | 23.0 | 6.0 | 18.0 | 9.0 |
| 4 | f0point5 | 0.46692804 | 0.13924657 | 0.46052632 | 0.49019608 | 0.32608697 | 0.28846154 | 0.45454547 | 0.5217391 | 0.41666666 | 0.234375 | 0.3649635 | 0.6060606 | 0.1744186 | 0.6097561 | 0.5 | 0.67164177 | 0.2777778 | 0.443038 | 0.625 | 0.34313726 | 0.52884614 | 0.7692308 | 0.6122449 | 0.40650406 | 0.4651163 | 0.5660377 | 0.45918366 | 0.5952381 | 0.36036035 | 0.625 | 0.45454547 | 0.35714287 |
| 5 | f1 | 0.45632124 | 0.110560715 | 0.4516129 | 0.41666666 | 0.31578946 | 0.35820895 | 0.5116279 | 0.55813956 | 0.36363637 | 0.27272728 | 0.42553192 | 0.53333336 | 0.20689656 | 0.5405405 | 0.3846154 | 0.64285713 | 0.3030303 | 0.4117647 | 0.5945946 | 0.3888889 | 0.5365854 | 0.64 | 0.54545456 | 0.47619048 | 0.5 | 0.5217391 | 0.47368422 | 0.5555556 | 0.41025642 | 0.5714286 | 0.47058824 | 0.30769232 |
| 6 | f2 | 0.45867836 | 0.10390178 | 0.443038 | 0.36231884 | 0.30612245 | 0.47244096 | 0.5851064 | 0.6 | 0.32258064 | 0.32608697 | 0.5102041 | 0.47619048 | 0.2542373 | 0.4854369 | 0.3125 | 0.6164383 | 0.33333334 | 0.3846154 | 0.5670103 | 0.44871795 | 0.5445545 | 0.5479452 | 0.4918033 | 0.57471263 | 0.5405405 | 0.48387095 | 0.48913044 | 0.5208333 | 0.47619048 | 0.5263158 | 0.4878049 | 0.27027026 |
| 7 | lift_top_group | 9.01935 | 3.257432 | 10.833333 | 11.555555 | 8.666667 | 8.666667 | 10.196078 | 9.122807 | 8.666667 | 0.0 | 15.294118 | 9.62963 | 8.666667 | 11.818182 | 9.62963 | 5.7777777 | 6.1904764 | 9.122807 | 12.95 | 6.1666665 | 8.633333 | 16.1875 | 13.282051 | 5.7555556 | 6.1666665 | 6.6410255 | 9.592592 | 8.633333 | 5.7555556 | 10.791667 | 5.3958335 | 10.791667 |
| 8 | logloss | 0.18337043 | 0.032842204 | 0.1876225 | 0.1853218 | 0.15685907 | 0.24966325 | 0.1874058 | 0.18505175 | 0.246197 | 0.13779983 | 0.19819152 | 0.1874985 | 0.1609587 | 0.22206269 | 0.22799125 | 0.14168519 | 0.19228448 | 0.22527215 | 0.19010212 | 0.18087101 | 0.19930607 | 0.15932454 | 0.15547653 | 0.17896676 | 0.15689974 | 0.14345263 | 0.20933427 | 0.20883945 | 0.18930222 | 0.12129969 | 0.18843003 | 0.1276423 |
| 9 | max_per_class_error | 0.5313485 | 0.11762855 | 0.5625 | 0.6666667 | 0.7 | 0.4 | 0.3529412 | 0.36842105 | 0.7 | 0.625 | 0.4117647 | 0.5555556 | 0.7 | 0.54545456 | 0.7222222 | 0.4 | 0.64285713 | 0.6315789 | 0.45 | 0.5 | 0.45 | 0.5 | 0.53846157 | 0.33333334 | 0.42857143 | 0.53846157 | 0.5 | 0.5 | 0.46666667 | 0.5 | 0.5 | 0.75 |
| 10 | mcc | 0.4313766 | 0.11506222 | 0.417132 | 0.40433297 | 0.29034942 | 0.314483 | 0.4823187 | 0.52310306 | 0.3311331 | 0.25351265 | 0.39146608 | 0.5177088 | 0.17438 | 0.51748455 | 0.39009342 | 0.6244224 | 0.26037762 | 0.3741919 | 0.5657891 | 0.3558803 | 0.49695313 | 0.65185744 | 0.5357057 | 0.45633632 | 0.47182348 | 0.5046277 | 0.43284053 | 0.5265611 | 0.3768111 | 0.5658994 | 0.43439713 | 0.29932582 |
| 11 | mean_per_class_accuracy | 0.7147494 | 0.054424644 | 0.7023566 | 0.6585034 | 0.638 | 0.7270833 | 0.79266524 | 0.7908932 | 0.6354167 | 0.6656746 | 0.7529654 | 0.7139578 | 0.618 | 0.7167685 | 0.63269055 | 0.79183674 | 0.65011615 | 0.667613 | 0.7624477 | 0.71938777 | 0.7540795 | 0.7479424 | 0.72467166 | 0.79849726 | 0.7653061 | 0.72263914 | 0.7271784 | 0.7374477 | 0.7338798 | 0.74601597 | 0.7294239 | 0.6190239 |
| 12 | mean_per_class_error | 0.2852506 | 0.054424644 | 0.29764345 | 0.3414966 | 0.362 | 0.27291667 | 0.20733479 | 0.20910679 | 0.36458334 | 0.3343254 | 0.24703461 | 0.28604224 | 0.382 | 0.28323147 | 0.36730945 | 0.20816326 | 0.34988385 | 0.33238697 | 0.2375523 | 0.28061223 | 0.24592051 | 0.2520576 | 0.27532834 | 0.20150273 | 0.23469388 | 0.27736086 | 0.27282158 | 0.2625523 | 0.26612023 | 0.25398406 | 0.27057612 | 0.38097608 |
| 13 | mse | 0.047172975 | 0.010062259 | 0.04888559 | 0.046554737 | 0.036305137 | 0.06667076 | 0.049683847 | 0.0510059 | 0.06478831 | 0.030207302 | 0.052116413 | 0.048295792 | 0.036631126 | 0.058904957 | 0.058934156 | 0.036906075 | 0.048691798 | 0.05977394 | 0.049625657 | 0.047268216 | 0.053533044 | 0.040459316 | 0.03701342 | 0.048762523 | 0.041314267 | 0.03638495 | 0.05464109 | 0.055399504 | 0.048425395 | 0.02751335 | 0.049752545 | 0.030740079 |
| 14 | null_deviance | 118.03537 | 21.421686 | 120.223526 | 114.7255 | 87.37807 | 142.31174 | 125.73113 | 136.7752 | 142.31174 | 76.50513 | 125.73113 | 131.24835 | 87.37807 | 153.41394 | 131.24835 | 114.7255 | 109.23703 | 136.7752 | 142.19029 | 109.11213 | 142.19029 | 120.09978 | 103.63261 | 114.60118 | 109.11213 | 103.63261 | 131.12575 | 142.19029 | 114.60118 | 76.37677 | 120.09978 | 76.37677 |
| 15 | pr_auc | 0.3289946 | 0.14216518 | 0.30546305 | 0.34000897 | 0.107951894 | 0.25836605 | 0.40915602 | 0.45839494 | 0.2970723 | 0.08474422 | 0.41740307 | 0.48763844 | 0.083102316 | 0.52515036 | 0.24085344 | 0.5193918 | 0.15094063 | 0.2903547 | 0.5837953 | 0.21639653 | 0.42677876 | 0.61288583 | 0.3958843 | 0.23024206 | 0.27695373 | 0.405081 | 0.3399414 | 0.4308292 | 0.27877495 | 0.2712737 | 0.26067606 | 0.16433305 |
| 16 | precision | 0.48201114 | 0.16940746 | 0.46666667 | 0.5555556 | 0.33333334 | 0.25531915 | 0.42307693 | 0.5 | 0.46153846 | 0.21428572 | 0.33333334 | 0.6666667 | 0.15789473 | 0.6666667 | 0.625 | 0.6923077 | 0.2631579 | 0.46666667 | 0.64705884 | 0.3181818 | 0.52380955 | 0.8888889 | 0.6666667 | 0.37037036 | 0.44444445 | 0.6 | 0.45 | 0.625 | 0.33333334 | 0.6666667 | 0.44444445 | 0.4 |
| 17 | r2 | 0.14914195 | 0.09675528 | 0.15351795 | 0.14364623 | 0.018309088 | 0.061053462 | 0.18696974 | 0.2469974 | 0.08756466 | -0.012903579 | 0.14716302 | 0.25050607 | 0.009494395 | 0.23950055 | 0.08541116 | 0.32112905 | 0.044260897 | 0.1175544 | 0.30356932 | 0.075568765 | 0.24873431 | 0.3019415 | 0.22360942 | 0.10627356 | 0.19201101 | 0.23679207 | 0.15505323 | 0.22254097 | 0.11245246 | 0.08086506 | 0.14160222 | -0.026929885 |
| 18 | recall | 0.46865144 | 0.11762855 | 0.4375 | 0.33333334 | 0.3 | 0.6 | 0.64705884 | 0.6315789 | 0.3 | 0.375 | 0.5882353 | 0.44444445 | 0.3 | 0.45454547 | 0.2777778 | 0.6 | 0.35714287 | 0.36842105 | 0.55 | 0.5 | 0.55 | 0.5 | 0.46153846 | 0.6666667 | 0.5714286 | 0.46153846 | 0.5 | 0.5 | 0.53333336 | 0.5 | 0.5 | 0.25 |
| 19 | residual_deviance | 95.19201 | 17.113794 | 97.5637 | 96.36733 | 81.56671 | 129.82489 | 97.45101 | 96.22691 | 128.02245 | 71.655914 | 103.05959 | 97.499214 | 83.698524 | 115.4726 | 118.55546 | 73.6763 | 99.98792 | 117.14152 | 98.47289 | 93.691185 | 103.24055 | 82.53011 | 80.536835 | 92.70478 | 81.27406 | 74.30846 | 108.43515 | 108.17883 | 98.05855 | 62.83324 | 97.60676 | 66.11871 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:11:18 | 0.000 sec | 2 | .89E1 | 15 | 0.452069 | 0.452454 | 0.452427 | 0.014922 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:11:18 | 0.003 sec | 4 | .56E1 | 15 | 0.450602 | 0.451268 | 0.451025 | 0.014849 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:11:18 | 0.005 sec | 6 | .34E1 | 15 | 0.448294 | 0.449405 | 0.448817 | 0.014734 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:11:18 | 0.008 sec | 8 | .21E1 | 15 | 0.444701 | 0.446508 | 0.445375 | 0.014556 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:11:18 | 0.011 sec | 10 | .13E1 | 15 | 0.439270 | 0.442140 | 0.440160 | 0.014291 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:11:18 | 0.013 sec | 12 | .83E0 | 15 | 0.431409 | 0.435843 | 0.432580 | 0.013915 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:11:18 | 0.016 sec | 14 | .51E0 | 15 | 0.420860 | 0.427445 | 0.422346 | 0.013425 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:11:18 | 0.018 sec | 16 | .32E0 | 15 | 0.408390 | 0.417623 | 0.410127 | 0.012877 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:11:18 | 0.021 sec | 18 | .2E0 | 15 | 0.396009 | 0.408041 | 0.397861 | 0.012385 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:11:18 | 0.023 sec | 20 | .12E0 | 15 | 0.385851 | 0.400388 | 0.387739 | 0.012047 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:11:18 | 0.026 sec | 22 | .76E-1 | 15 | 0.378718 | 0.395230 | 0.380675 | 0.011877 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:11:18 | 0.029 sec | 24 | .47E-1 | 15 | 0.374173 | 0.392135 | 0.376288 | 0.011828 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:11:18 | 0.031 sec | 26 | .29E-1 | 15 | 0.371398 | 0.390418 | 0.373748 | 0.011848 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:11:18 | 0.034 sec | 28 | .18E-1 | 15 | 0.369710 | 0.389525 | 0.372337 | 0.011901 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:11:18 | 0.039 sec | 30 | .11E-1 | 15 | 0.368679 | 0.389112 | 0.371551 | 0.011971 | 0.0 | 30.0 | 0.217996 | 0.184026 | 0.158821 | 0.779402 | 0.31251 | 9.598126 | 0.071025 | 0.224215 | 0.194489 | 0.109931 | 0.745708 | 0.221553 | 4.992308 | 0.078069 | |
| 15 | 2021-07-15 20:11:18 | 0.042 sec | 32 | .7E-2 | 15 | 0.368053 | 0.388979 | 0.371333 | 0.011990 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:11:18 | 0.044 sec | 34 | .44E-2 | 15 | 0.367681 | 0.389006 | 0.373396 | 0.012335 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:11:18 | 0.050 sec | 36 | .27E-2 | 15 | 0.367466 | 0.389112 | 0.374819 | 0.013044 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:11:18 | 0.052 sec | 37 | .17E-2 | 15 | 0.367338 | 0.389250 | 0.376569 | 0.013254 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.578155 | 1.000000 | 0.271839 |
| 1 | Average_Transaction_Frequency | 0.258025 | 0.446290 | 0.121319 |
| 2 | Merchant_ID | 0.208916 | 0.361349 | 0.098229 |
| 3 | Card_Type.1 | 0.201374 | 0.348304 | 0.094683 |
| 4 | Card_Type.0 | 0.199199 | 0.344543 | 0.093660 |
| 5 | Channel_ID | 0.179380 | 0.310262 | 0.084341 |
| 6 | Minimum_Transaction_Amount | 0.169619 | 0.293380 | 0.079752 |
| 7 | Transaction_Amount | 0.107500 | 0.185937 | 0.050545 |
| 8 | Transaction_Date | 0.080574 | 0.139363 | 0.037884 |
| 9 | Average_Transaction_Amount | 0.057392 | 0.099268 | 0.026985 |
| 10 | Day | 0.030577 | 0.052888 | 0.014377 |
| 11 | Maximum_Transaction_Amount | 0.025478 | 0.044067 | 0.011979 |
| 12 | Month | 0.016241 | 0.028092 | 0.007636 |
| 13 | City_ID | 0.014397 | 0.024902 | 0.006769 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201121 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.006532 ) | nlambda = 30, lambda.max = 8.2891, lambda.min = 0.006532, lambda.1... | 14 | 14 | 32 | automl_training_py_35_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04857215820162223 RMSE: 0.22039092132304866 LogLoss: 0.18802498074251722 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938334 Residual deviance: 2927.925000122479 AIC: 2957.925000122479 AUC: 0.7682298418838457 AUCPR: 0.2791556316482742 Gini: 0.5364596837676914 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.25416418043946476:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7012.0 | 306.0 | 0.0418 | (306.0/7318.0) |
| 1 | 1 | 274.0 | 194.0 | 0.5855 | (274.0/468.0) |
| 2 | Total | 7286.0 | 500.0 | 0.0745 | (580.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.254164 | 0.400826 | 149.0 |
| 1 | max f2 | 0.066169 | 0.428465 | 230.0 |
| 2 | max f0point5 | 0.305003 | 0.407088 | 123.0 |
| 3 | max accuracy | 0.442060 | 0.940406 | 40.0 |
| 4 | max precision | 0.827848 | 1.000000 | 0.0 |
| 5 | max recall | 0.017389 | 1.000000 | 383.0 |
| 6 | max specificity | 0.827848 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.254164 | 0.361382 | 149.0 |
| 8 | max min_per_class_accuracy | 0.042450 | 0.681624 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.063060 | 0.707160 | 234.0 |
| 10 | max tns | 0.827848 | 7318.000000 | 0.0 |
| 11 | max fns | 0.827848 | 467.000000 | 0.0 |
| 12 | max fps | 0.000938 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017389 | 468.000000 | 383.0 |
| 14 | max tnr | 0.827848 | 1.000000 | 0.0 |
| 15 | max fnr | 0.827848 | 0.997863 | 0.0 |
| 16 | max fpr | 0.000938 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017389 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.425251 | 8.318376 | 8.318376 | 0.500000 | 0.504983 | 0.500000 | 0.504983 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.385907 | 7.465209 | 7.891793 | 0.448718 | 0.402645 | 0.474359 | 0.453814 | 0.074786 | 0.158120 | 646.520929 | 689.179268 | 0.146914 |
| 2 | 3 | 0.030054 | 0.356530 | 7.251918 | 7.678501 | 0.435897 | 0.370036 | 0.461538 | 0.425888 | 0.072650 | 0.230769 | 625.191760 | 667.850099 | 0.213551 |
| 3 | 4 | 0.040072 | 0.336618 | 6.398751 | 7.358563 | 0.384615 | 0.345287 | 0.442308 | 0.405738 | 0.064103 | 0.294872 | 539.875082 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.311853 | 5.119001 | 6.910651 | 0.307692 | 0.324637 | 0.415385 | 0.389517 | 0.051282 | 0.346154 | 411.900066 | 591.065089 | 0.314998 |
| 5 | 6 | 0.100051 | 0.068038 | 2.694384 | 4.805224 | 0.161954 | 0.162521 | 0.288832 | 0.276165 | 0.134615 | 0.480769 | 169.438402 | 380.522366 | 0.405065 |
| 6 | 7 | 0.150013 | 0.053578 | 0.855360 | 3.489730 | 0.051414 | 0.059076 | 0.209760 | 0.203864 | 0.042735 | 0.523504 | -14.463999 | 248.972969 | 0.397377 |
| 7 | 8 | 0.200103 | 0.048859 | 1.066458 | 2.883134 | 0.064103 | 0.050963 | 0.173299 | 0.165590 | 0.053419 | 0.576923 | 6.645847 | 188.313420 | 0.400919 |
| 8 | 9 | 0.300026 | 0.043784 | 0.812592 | 2.193544 | 0.048843 | 0.046085 | 0.131849 | 0.125789 | 0.081197 | 0.658120 | -18.740799 | 119.354437 | 0.380995 |
| 9 | 10 | 0.400077 | 0.040092 | 0.726123 | 1.826571 | 0.043646 | 0.041845 | 0.109791 | 0.104796 | 0.072650 | 0.730769 | -27.387731 | 82.657118 | 0.351841 |
| 10 | 11 | 0.500000 | 0.037186 | 0.684288 | 1.598291 | 0.041131 | 0.038627 | 0.096070 | 0.091572 | 0.068376 | 0.799145 | -31.571199 | 59.829060 | 0.318276 |
| 11 | 12 | 0.600051 | 0.034513 | 0.726123 | 1.452867 | 0.043646 | 0.035848 | 0.087329 | 0.082281 | 0.072650 | 0.871795 | -27.387731 | 45.286705 | 0.289122 |
| 12 | 13 | 0.699974 | 0.031589 | 0.384912 | 1.300414 | 0.023136 | 0.033054 | 0.078165 | 0.075254 | 0.038462 | 0.910256 | -61.508800 | 30.041402 | 0.223730 |
| 13 | 14 | 0.800026 | 0.028206 | 0.320348 | 1.177847 | 0.019255 | 0.029974 | 0.070798 | 0.069591 | 0.032051 | 0.942308 | -67.965176 | 17.784680 | 0.151381 |
| 14 | 15 | 0.899949 | 0.023525 | 0.320760 | 1.082683 | 0.019280 | 0.026037 | 0.065078 | 0.064755 | 0.032051 | 0.974359 | -67.924000 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.000692 | 0.256279 | 1.000000 | 0.015404 | 0.018306 | 0.060108 | 0.060108 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.046495596727669106 RMSE: 0.2156283764435217 LogLoss: 0.17860301973264647 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311492 Residual deviance: 695.4801588389255 AIC: 725.4801588389255 AUC: 0.792438466208958 AUCPR: 0.32668709473869484 Gini: 0.5848769324179159 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2723607961884822:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1768.0 | 62.0 | 0.0339 | (62.0/1830.0) |
| 1 | 1 | 63.0 | 54.0 | 0.5385 | (63.0/117.0) |
| 2 | Total | 1831.0 | 116.0 | 0.0642 | (125.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.272361 | 0.463519 | 96.0 |
| 1 | max f2 | 0.075372 | 0.509404 | 139.0 |
| 2 | max f0point5 | 0.291926 | 0.467890 | 87.0 |
| 3 | max accuracy | 0.418666 | 0.941448 | 13.0 |
| 4 | max precision | 0.586764 | 1.000000 | 0.0 |
| 5 | max recall | 0.019987 | 1.000000 | 367.0 |
| 6 | max specificity | 0.586764 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.272361 | 0.429381 | 96.0 |
| 8 | max min_per_class_accuracy | 0.044083 | 0.726496 | 226.0 |
| 9 | max mean_per_class_accuracy | 0.053603 | 0.763311 | 184.0 |
| 10 | max tns | 0.586764 | 1830.000000 | 0.0 |
| 11 | max fns | 0.586764 | 116.000000 | 0.0 |
| 12 | max fps | 0.000947 | 1830.000000 | 399.0 |
| 13 | max tps | 0.019987 | 117.000000 | 367.0 |
| 14 | max tnr | 0.586764 | 1.000000 | 0.0 |
| 15 | max fnr | 0.586764 | 0.991453 | 0.0 |
| 16 | max fpr | 0.000947 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019987 | 1.000000 | 367.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.89 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.413237 | 8.320513 | 8.320513 | 0.500000 | 0.454803 | 0.500000 | 0.454803 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.378547 | 7.882591 | 8.107166 | 0.473684 | 0.395600 | 0.487179 | 0.425961 | 0.076923 | 0.162393 | 688.259109 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.357108 | 4.992308 | 7.051282 | 0.300000 | 0.366802 | 0.423729 | 0.405907 | 0.051282 | 0.213675 | 399.230769 | 605.128205 | 0.195096 |
| 3 | 4 | 0.040062 | 0.335906 | 9.634278 | 7.680473 | 0.578947 | 0.345012 | 0.461538 | 0.391074 | 0.094017 | 0.307692 | 863.427800 | 668.047337 | 0.284741 |
| 4 | 5 | 0.050334 | 0.306114 | 9.152564 | 7.980900 | 0.550000 | 0.320887 | 0.479592 | 0.376750 | 0.094017 | 0.401709 | 815.256410 | 698.090005 | 0.373841 |
| 5 | 6 | 0.100154 | 0.065814 | 3.259582 | 5.632347 | 0.195876 | 0.149180 | 0.338462 | 0.263549 | 0.162393 | 0.564103 | 225.958234 | 463.234714 | 0.493611 |
| 6 | 7 | 0.149974 | 0.054028 | 1.544013 | 4.274236 | 0.092784 | 0.058728 | 0.256849 | 0.195509 | 0.076923 | 0.641026 | 54.401269 | 327.423604 | 0.522446 |
| 7 | 8 | 0.200308 | 0.048576 | 0.339613 | 3.285536 | 0.020408 | 0.051136 | 0.197436 | 0.159230 | 0.017094 | 0.658120 | -66.038723 | 228.553583 | 0.487081 |
| 8 | 9 | 0.299949 | 0.043804 | 0.686228 | 2.422067 | 0.041237 | 0.045867 | 0.145548 | 0.121572 | 0.068376 | 0.726496 | -31.377214 | 142.206709 | 0.453818 |
| 9 | 10 | 0.400103 | 0.040478 | 0.341354 | 1.901221 | 0.020513 | 0.042151 | 0.114249 | 0.101691 | 0.034188 | 0.760684 | -65.864563 | 90.122116 | 0.383635 |
| 10 | 11 | 0.500257 | 0.037394 | 0.426693 | 1.606013 | 0.025641 | 0.038957 | 0.096509 | 0.089131 | 0.042735 | 0.803419 | -57.330703 | 60.601274 | 0.322544 |
| 11 | 12 | 0.599897 | 0.034389 | 0.428892 | 1.410498 | 0.025773 | 0.035801 | 0.084760 | 0.080273 | 0.042735 | 0.846154 | -57.110759 | 41.049789 | 0.262001 |
| 12 | 13 | 0.700051 | 0.031757 | 0.256016 | 1.245330 | 0.015385 | 0.033067 | 0.074835 | 0.073520 | 0.025641 | 0.871795 | -74.398422 | 24.532987 | 0.182724 |
| 13 | 14 | 0.799692 | 0.028399 | 0.600449 | 1.164979 | 0.036082 | 0.030235 | 0.070006 | 0.068127 | 0.059829 | 0.931624 | -39.955062 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.023668 | 0.426693 | 1.082806 | 0.025641 | 0.026047 | 0.065068 | 0.063443 | 0.042735 | 0.974359 | -57.330703 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.000837 | 0.256016 | 1.000000 | 0.015385 | 0.018182 | 0.060092 | 0.058910 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04873391639912115 RMSE: 0.22075759646979567 LogLoss: 0.18966440439790905 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.637296376292 Residual deviance: 2953.4541052842396 AIC: 2983.4541052842396 AUC: 0.7515232899559218 AUCPR: 0.2723526102008065 Gini: 0.5030465799118435 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.11829822610994437:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6980.0 | 338.0 | 0.0462 | (338.0/7318.0) |
| 1 | 1 | 266.0 | 202.0 | 0.5684 | (266.0/468.0) |
| 2 | Total | 7246.0 | 540.0 | 0.0776 | (604.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.118298 | 0.400794 | 189.0 |
| 1 | max f2 | 0.062424 | 0.420387 | 231.0 |
| 2 | max f0point5 | 0.305414 | 0.413286 | 117.0 |
| 3 | max accuracy | 0.420758 | 0.940663 | 44.0 |
| 4 | max precision | 0.825835 | 1.000000 | 0.0 |
| 5 | max recall | 0.016316 | 1.000000 | 384.0 |
| 6 | max specificity | 0.825835 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.221890 | 0.361377 | 159.0 |
| 8 | max min_per_class_accuracy | 0.043686 | 0.669445 | 280.0 |
| 9 | max mean_per_class_accuracy | 0.062424 | 0.701141 | 231.0 |
| 10 | max tns | 0.825835 | 7318.000000 | 0.0 |
| 11 | max fns | 0.825835 | 467.000000 | 0.0 |
| 12 | max fps | 0.000928 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016316 | 468.000000 | 384.0 |
| 14 | max tnr | 0.825835 | 1.000000 | 0.0 |
| 15 | max fnr | 0.825835 | 0.997863 | 0.0 |
| 16 | max fpr | 0.000928 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016316 | 1.000000 | 384.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.00 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.420697 | 8.744959 | 8.744959 | 0.525641 | 0.502838 | 0.525641 | 0.502838 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.379848 | 7.251918 | 7.998439 | 0.435897 | 0.398132 | 0.480769 | 0.450485 | 0.072650 | 0.160256 | 625.191760 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.348871 | 7.038626 | 7.678501 | 0.423077 | 0.364404 | 0.461538 | 0.421791 | 0.070513 | 0.230769 | 603.862590 | 667.850099 | 0.213551 |
| 3 | 4 | 0.040072 | 0.329732 | 6.398751 | 7.358563 | 0.384615 | 0.339837 | 0.442308 | 0.401303 | 0.064103 | 0.294872 | 539.875082 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.300396 | 5.332292 | 6.953309 | 0.320513 | 0.315074 | 0.417949 | 0.384057 | 0.053419 | 0.348291 | 433.229235 | 595.330923 | 0.317271 |
| 5 | 6 | 0.100051 | 0.063827 | 2.523312 | 4.741154 | 0.151671 | 0.137477 | 0.284981 | 0.260925 | 0.126068 | 0.474359 | 152.331202 | 374.115401 | 0.398245 |
| 6 | 7 | 0.175700 | 0.060325 | 0.706144 | 3.003858 | 0.042445 | 0.060940 | 0.180556 | 0.174820 | 0.053419 | 0.527778 | -29.385602 | 200.385802 | 0.374594 |
| 7 | 8 | 0.200103 | 0.054462 | 0.437809 | 2.690925 | 0.026316 | 0.057170 | 0.161746 | 0.160473 | 0.010684 | 0.538462 | -56.219073 | 169.092525 | 0.359997 |
| 8 | 9 | 0.300026 | 0.045913 | 1.005048 | 2.129447 | 0.060411 | 0.049340 | 0.127997 | 0.123460 | 0.100427 | 0.638889 | 0.504801 | 112.944730 | 0.360534 |
| 9 | 10 | 0.400077 | 0.041583 | 0.683410 | 1.767822 | 0.041078 | 0.043692 | 0.106260 | 0.103512 | 0.068376 | 0.707265 | -31.659041 | 76.782182 | 0.326833 |
| 10 | 11 | 0.500000 | 0.038204 | 0.620136 | 1.538462 | 0.037275 | 0.039807 | 0.092474 | 0.090781 | 0.061966 | 0.769231 | -37.986399 | 53.846154 | 0.286449 |
| 11 | 12 | 0.600051 | 0.035295 | 0.726123 | 1.403014 | 0.043646 | 0.036731 | 0.084332 | 0.081769 | 0.072650 | 0.841880 | -27.387731 | 40.301377 | 0.257294 |
| 12 | 13 | 0.699974 | 0.032230 | 0.598752 | 1.288204 | 0.035990 | 0.033765 | 0.077431 | 0.074916 | 0.059829 | 0.901709 | -40.124800 | 28.820356 | 0.214636 |
| 13 | 14 | 0.800026 | 0.028855 | 0.320348 | 1.167163 | 0.019255 | 0.030608 | 0.070156 | 0.069375 | 0.032051 | 0.933761 | -67.965176 | 16.716338 | 0.142288 |
| 14 | 15 | 0.899949 | 0.023951 | 0.363528 | 1.077934 | 0.021851 | 0.026594 | 0.064792 | 0.064625 | 0.036325 | 0.970085 | -63.647200 | 7.793428 | 0.074622 |
| 15 | 16 | 1.000000 | 0.000326 | 0.298992 | 1.000000 | 0.017972 | 0.018671 | 0.060108 | 0.060027 | 0.029915 | 1.000000 | -70.100831 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9293634 | 0.025219189 | 0.95 | 0.9230769 | 0.9269231 | 0.95384616 | 0.95384616 | 0.9230769 | 0.96153843 | 0.9153846 | 0.85384613 | 0.9153846 | 0.93846154 | 0.9346154 | 0.9269231 | 0.88846153 | 0.9115385 | 0.9692308 | 0.8996139 | 0.9189189 | 0.9189189 | 0.9459459 | 0.93436295 | 0.9150579 | 0.9227799 | 0.96525097 | 0.9498069 | 0.8918919 | 0.9459459 | 0.93050194 | 0.95752895 | 0.93822396 |
| 1 | auc | 0.76348716 | 0.071166314 | 0.76800233 | 0.71019876 | 0.79448074 | 0.85323113 | 0.76584023 | 0.73025554 | 0.6888441 | 0.7904345 | 0.7159797 | 0.77731866 | 0.736168 | 0.8148148 | 0.54239255 | 0.7096774 | 0.7122175 | 0.8783602 | 0.78377193 | 0.73764855 | 0.7493421 | 0.8430269 | 0.82401556 | 0.7942464 | 0.71366066 | 0.8042877 | 0.85510206 | 0.832636 | 0.77339786 | 0.7363683 | 0.8395722 | 0.6293223 |
| 2 | err | 0.07063657 | 0.025219189 | 0.05 | 0.07692308 | 0.073076926 | 0.046153847 | 0.046153847 | 0.07692308 | 0.03846154 | 0.08461539 | 0.14615385 | 0.08461539 | 0.06153846 | 0.06538462 | 0.073076926 | 0.11153846 | 0.08846154 | 0.03076923 | 0.1003861 | 0.08108108 | 0.08108108 | 0.054054055 | 0.06563707 | 0.08494209 | 0.077220075 | 0.034749035 | 0.05019305 | 0.10810811 | 0.054054055 | 0.06949807 | 0.042471044 | 0.06177606 |
| 3 | err_count | 18.333334 | 6.5513005 | 13.0 | 20.0 | 19.0 | 12.0 | 12.0 | 20.0 | 10.0 | 22.0 | 38.0 | 22.0 | 16.0 | 17.0 | 19.0 | 29.0 | 23.0 | 8.0 | 26.0 | 21.0 | 21.0 | 14.0 | 17.0 | 22.0 | 20.0 | 9.0 | 13.0 | 28.0 | 14.0 | 18.0 | 11.0 | 16.0 |
| 4 | f0point5 | 0.45547372 | 0.15147571 | 0.530303 | 0.4225352 | 0.4494382 | 0.42553192 | 0.6818182 | 0.32051283 | 0.5681818 | 0.29126215 | 0.12269939 | 0.4385965 | 0.4861111 | 0.505618 | 0.3030303 | 0.20833333 | 0.5092593 | 0.6818182 | 0.36036035 | 0.29411766 | 0.43956044 | 0.49382716 | 0.46153846 | 0.30927834 | 0.36231884 | 0.8064516 | 0.530303 | 0.36290324 | 0.6034483 | 0.48076922 | 0.71428573 | 0.5 |
| 5 | f1 | 0.43831208 | 0.11235892 | 0.5185185 | 0.375 | 0.45714286 | 0.4 | 0.6 | 0.33333334 | 0.5 | 0.3529412 | 0.17391305 | 0.47619048 | 0.46666667 | 0.51428574 | 0.2962963 | 0.25641027 | 0.4888889 | 0.6 | 0.3809524 | 0.32258064 | 0.43243244 | 0.53333336 | 0.41379312 | 0.3529412 | 0.33333334 | 0.6896552 | 0.5185185 | 0.39130434 | 0.5 | 0.5263158 | 0.56 | 0.3846154 |
| 6 | f2 | 0.43535635 | 0.089564964 | 0.5072464 | 0.33707866 | 0.4651163 | 0.3773585 | 0.53571427 | 0.3472222 | 0.44642857 | 0.4477612 | 0.29850745 | 0.5208333 | 0.44871795 | 0.5232558 | 0.28985506 | 0.33333334 | 0.47008547 | 0.53571427 | 0.4040404 | 0.35714287 | 0.42553192 | 0.5797101 | 0.375 | 0.41095892 | 0.30864197 | 0.60240966 | 0.5072464 | 0.4245283 | 0.42682928 | 0.5813953 | 0.46052632 | 0.3125 |
| 7 | lift_top_group | 8.089831 | 5.756644 | 12.380953 | 9.122807 | 5.098039 | 15.757576 | 14.444445 | 0.0 | 7.2222223 | 0.0 | 0.0 | 9.62963 | 10.833333 | 5.098039 | 0.0 | 0.0 | 7.2222223 | 21.666666 | 13.631579 | 6.6410255 | 4.5438595 | 6.6410255 | 0.0 | 13.282051 | 5.0784316 | 14.388889 | 6.1666665 | 8.633333 | 14.388889 | 10.791667 | 15.235294 | 4.796296 |
| 8 | logloss | 0.18792725 | 0.032492485 | 0.16183199 | 0.23102114 | 0.20027487 | 0.13980255 | 0.19036962 | 0.18677619 | 0.15548834 | 0.15646584 | 0.1322693 | 0.20070384 | 0.19327386 | 0.19256084 | 0.20043705 | 0.17882729 | 0.26566097 | 0.13172741 | 0.22183247 | 0.18320592 | 0.22141643 | 0.14802365 | 0.20704313 | 0.1712321 | 0.22069813 | 0.17449671 | 0.15825497 | 0.22827521 | 0.20340662 | 0.17768437 | 0.17275468 | 0.23200172 |
| 9 | max_per_class_error | 0.55388075 | 0.09442896 | 0.5 | 0.68421054 | 0.5294118 | 0.6363636 | 0.5 | 0.64285713 | 0.5833333 | 0.45454547 | 0.42857143 | 0.44444445 | 0.5625 | 0.47058824 | 0.71428573 | 0.5833333 | 0.5416667 | 0.5 | 0.57894737 | 0.61538464 | 0.57894737 | 0.3846154 | 0.64705884 | 0.53846157 | 0.7058824 | 0.44444445 | 0.5 | 0.55 | 0.6111111 | 0.375 | 0.5882353 | 0.7222222 |
| 10 | mcc | 0.4140981 | 0.11867936 | 0.49256343 | 0.3424201 | 0.41819814 | 0.37829927 | 0.58992136 | 0.29344437 | 0.49154666 | 0.33825287 | 0.19631688 | 0.43646932 | 0.43525764 | 0.47948983 | 0.25800943 | 0.22556594 | 0.44188073 | 0.59769464 | 0.3286362 | 0.28485996 | 0.38897252 | 0.5103111 | 0.3866006 | 0.32041422 | 0.29609627 | 0.6953352 | 0.49245626 | 0.33654246 | 0.49686882 | 0.497063 | 0.5834707 | 0.3899857 |
| 11 | mean_per_class_accuracy | 0.70338625 | 0.04551903 | 0.7378049 | 0.64337194 | 0.714718 | 0.671778 | 0.74380165 | 0.6562137 | 0.70228493 | 0.7385907 | 0.71654433 | 0.74885213 | 0.7044057 | 0.7461874 | 0.62456447 | 0.6639785 | 0.7079802 | 0.74596775 | 0.6792763 | 0.6658849 | 0.689693 | 0.7893996 | 0.6640739 | 0.70028144 | 0.6305299 | 0.7757031 | 0.7377551 | 0.6894351 | 0.6882204 | 0.78780866 | 0.70381624 | 0.6326648 |
| 12 | mean_per_class_error | 0.29661378 | 0.04551903 | 0.2621951 | 0.3566281 | 0.28528202 | 0.32822198 | 0.25619835 | 0.3437863 | 0.29771507 | 0.26140928 | 0.28345567 | 0.25114784 | 0.29559427 | 0.25381264 | 0.37543553 | 0.3360215 | 0.29201978 | 0.25403225 | 0.32072368 | 0.33411506 | 0.31030703 | 0.21060038 | 0.33592612 | 0.2997186 | 0.3694701 | 0.22429691 | 0.2622449 | 0.31056485 | 0.31177962 | 0.21219136 | 0.29618376 | 0.36733517 |
| 13 | mse | 0.048360467 | 0.009946226 | 0.041215982 | 0.05927394 | 0.053386357 | 0.03490668 | 0.04873468 | 0.04785549 | 0.03744534 | 0.039178222 | 0.027377248 | 0.05329377 | 0.049598545 | 0.051712077 | 0.049356885 | 0.043730482 | 0.0722259 | 0.031440783 | 0.059075277 | 0.04569484 | 0.059118953 | 0.038147986 | 0.0559402 | 0.043693185 | 0.057063483 | 0.044180162 | 0.041129515 | 0.06255136 | 0.052951276 | 0.045717184 | 0.04519618 | 0.05962203 |
| 14 | null_deviance | 118.02124 | 19.058928 | 109.23703 | 136.7752 | 125.73113 | 92.82863 | 131.24835 | 109.23703 | 98.28862 | 92.82863 | 71.082695 | 131.24835 | 120.223526 | 125.73113 | 109.23703 | 98.28862 | 164.55519 | 98.28862 | 136.65318 | 103.63261 | 136.65318 | 103.63261 | 125.607956 | 103.63261 | 125.607956 | 131.12575 | 109.11213 | 142.19029 | 131.12575 | 120.09978 | 125.607956 | 131.12575 |
| 15 | pr_auc | 0.30679077 | 0.13954958 | 0.29536867 | 0.32186282 | 0.28712222 | 0.36642668 | 0.5025384 | 0.15924418 | 0.25598085 | 0.1574329 | 0.06026311 | 0.37355992 | 0.31441343 | 0.31143665 | 0.10196809 | 0.102661744 | 0.3163861 | 0.5751905 | 0.3811229 | 0.15434223 | 0.26492032 | 0.3232288 | 0.26717553 | 0.24867943 | 0.17561266 | 0.6126563 | 0.33455437 | 0.34606618 | 0.45739362 | 0.31824458 | 0.58488417 | 0.23298557 |
| 16 | precision | 0.47607982 | 0.1905555 | 0.53846157 | 0.46153846 | 0.44444445 | 0.44444445 | 0.75 | 0.3125 | 0.625 | 0.26086956 | 0.102564104 | 0.41666666 | 0.5 | 0.5 | 0.30769232 | 0.18518518 | 0.52380955 | 0.75 | 0.3478261 | 0.2777778 | 0.44444445 | 0.47058824 | 0.5 | 0.2857143 | 0.3846154 | 0.90909094 | 0.53846157 | 0.34615386 | 0.7 | 0.45454547 | 0.875 | 0.625 |
| 17 | r2 | 0.13546097 | 0.08348212 | 0.19099875 | 0.12493597 | 0.12638155 | 0.13848421 | 0.24369505 | 0.060676213 | 0.14942709 | 0.033060268 | -0.045003917 | 0.17294337 | 0.1411727 | 0.15377963 | 0.031206282 | 0.006659745 | 0.13798183 | 0.2858209 | 0.1309586 | 0.041508563 | 0.13031615 | 0.19981082 | 0.087864734 | 0.08349518 | 0.069549 | 0.3168166 | 0.1956242 | 0.122174166 | 0.18118382 | 0.21122575 | 0.2630518 | 0.0780301 |
| 18 | recall | 0.44611925 | 0.09442896 | 0.5 | 0.31578946 | 0.47058824 | 0.36363637 | 0.5 | 0.35714287 | 0.41666666 | 0.54545456 | 0.5714286 | 0.5555556 | 0.4375 | 0.5294118 | 0.2857143 | 0.41666666 | 0.45833334 | 0.5 | 0.42105263 | 0.3846154 | 0.42105263 | 0.61538464 | 0.3529412 | 0.46153846 | 0.29411766 | 0.5555556 | 0.5 | 0.45 | 0.3888889 | 0.625 | 0.4117647 | 0.2777778 |
| 19 | residual_deviance | 97.54081 | 16.838604 | 84.15263 | 120.13099 | 104.14293 | 72.69732 | 98.9922 | 97.12362 | 80.853935 | 81.36224 | 68.78003 | 104.366 | 100.5024 | 100.13164 | 104.22727 | 92.99019 | 138.1437 | 68.49825 | 114.90922 | 94.900665 | 114.6937 | 76.67625 | 107.24834 | 88.69823 | 114.32163 | 90.3893 | 81.976074 | 118.24656 | 105.364624 | 92.0405 | 89.48692 | 120.17689 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:11:29 | 0.000 sec | 2 | .83E1 | 15.0 | 0.452188 | 0.451742 | 0.452548 | 0.013354 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:11:29 | 0.003 sec | 4 | .51E1 | 15.0 | 0.450794 | 0.450132 | 0.451216 | 0.013303 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:11:29 | 0.005 sec | 6 | .32E1 | 15.0 | 0.448605 | 0.447601 | 0.449123 | 0.013224 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:11:29 | 0.008 sec | 8 | .2E1 | 15.0 | 0.445206 | 0.443662 | 0.445869 | 0.013102 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:11:29 | 0.010 sec | 10 | .12E1 | 15.0 | 0.44009 | 0.437714 | 0.44096 | 0.012923 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:11:29 | 0.013 sec | 12 | .77E0 | 15.0 | 0.432741 | 0.429122 | 0.43388 | 0.012672 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:11:29 | 0.016 sec | 14 | .48E0 | 15.0 | 0.42299 | 0.417608 | 0.424423 | 0.012355 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:11:29 | 0.019 sec | 16 | .3E0 | 15.0 | 0.411642 | 0.403982 | 0.413311 | 0.012019 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:11:29 | 0.021 sec | 18 | .18E0 | 15.0 | 0.400571 | 0.390321 | 0.402363 | 0.01174 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:11:29 | 0.024 sec | 20 | .11E0 | 15.0 | 0.391615 | 0.378829 | 0.393463 | 0.011579 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:11:29 | 0.027 sec | 22 | .71E-1 | 15.0 | 0.385358 | 0.370424 | 0.387295 | 0.01153 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:11:29 | 0.029 sec | 24 | .44E-1 | 15.0 | 0.381367 | 0.364834 | 0.383465 | 0.011554 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:11:29 | 0.032 sec | 26 | .27E-1 | 15.0 | 0.378925 | 0.361326 | 0.381244 | 0.011611 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:11:29 | 0.035 sec | 28 | .17E-1 | 15.0 | 0.377446 | 0.359196 | 0.380017 | 0.011679 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:11:29 | 0.038 sec | 30 | .11E-1 | 15.0 | 0.376563 | 0.357939 | 0.379458 | 0.011725 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:11:29 | 0.275 sec | 31 | None | NaN | 31.0 | 0.220391 | 0.188025 | 0.140239 | 0.76823 | 0.279156 | 8.318376 | 0.074493 | 0.215628 | 0.178603 | 0.176797 | 0.792438 | 0.326687 | 8.320513 | 0.064201 | ||||||
| 16 | 2021-07-15 20:11:29 | 0.041 sec | 32 | .65E-2 | 15.0 | 0.37605 | 0.357206 | 0.37935 | 0.011816 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:11:29 | 0.048 sec | 34 | .41E-2 | 15.0 | 0.375766 | 0.356785 | 0.380552 | 0.011876 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:11:29 | 0.050 sec | 35 | .25E-2 | 15.0 | 0.37562 | 0.356538 | 0.38289 | 0.012123 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:11:29 | 0.052 sec | 36 | .16E-2 | 15.0 | 0.375546 | 0.356391 | 0.388923 | 0.013449 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.550146 | 1.000000 | 0.259177 |
| 1 | Average_Transaction_Frequency | 0.285308 | 0.518605 | 0.134410 |
| 2 | Merchant_ID | 0.213085 | 0.387325 | 0.100385 |
| 3 | Card_Type.1 | 0.175914 | 0.319759 | 0.082874 |
| 4 | Card_Type.0 | 0.173453 | 0.315286 | 0.081715 |
| 5 | Minimum_Transaction_Amount | 0.166327 | 0.302333 | 0.078358 |
| 6 | Channel_ID | 0.158249 | 0.287649 | 0.074552 |
| 7 | Transaction_Amount | 0.123418 | 0.224337 | 0.058143 |
| 8 | Transaction_Date | 0.068539 | 0.124583 | 0.032289 |
| 9 | Average_Transaction_Amount | 0.065853 | 0.119702 | 0.031024 |
| 10 | Day | 0.056949 | 0.103516 | 0.026829 |
| 11 | Month | 0.049970 | 0.090831 | 0.023541 |
| 12 | Maximum_Transaction_Amount | 0.031359 | 0.057002 | 0.014774 |
| 13 | City_ID | 0.004096 | 0.007445 | 0.001930 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201131 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01108 ) | nlambda = 30, lambda.max = 8.7349, lambda.min = 0.01108, lambda.1s... | 14 | 14 | 30 | automl_training_py_70_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.048021144888352664 RMSE: 0.21913727407347355 LogLoss: 0.18595259632828265 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938398 Residual deviance: 2895.653830024017 AIC: 2925.653830024017 AUC: 0.7780309294725802 AUCPR: 0.2967405900032143 Gini: 0.5560618589451605 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.24901650473363693:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7017.0 | 301.0 | 0.0411 | (301.0/7318.0) |
| 1 | 1 | 270.0 | 198.0 | 0.5769 | (270.0/468.0) |
| 2 | Total | 7287.0 | 499.0 | 0.0733 | (571.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.249017 | 0.409514 | 148.0 |
| 1 | max f2 | 0.063017 | 0.444860 | 224.0 |
| 2 | max f0point5 | 0.329175 | 0.419207 | 109.0 |
| 3 | max accuracy | 0.460462 | 0.940791 | 26.0 |
| 4 | max precision | 0.563149 | 0.714286 | 11.0 |
| 5 | max recall | 0.018877 | 1.000000 | 381.0 |
| 6 | max specificity | 0.836944 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.249017 | 0.370677 | 148.0 |
| 8 | max min_per_class_accuracy | 0.042608 | 0.698718 | 279.0 |
| 9 | max mean_per_class_accuracy | 0.049559 | 0.716690 | 252.0 |
| 10 | max tns | 0.836944 | 7317.000000 | 0.0 |
| 11 | max fns | 0.836944 | 467.000000 | 0.0 |
| 12 | max fps | 0.001718 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018877 | 468.000000 | 381.0 |
| 14 | max tnr | 0.836944 | 0.999863 | 0.0 |
| 15 | max fnr | 0.836944 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001718 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018877 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.428837 | 8.744959 | 8.744959 | 0.525641 | 0.499051 | 0.525641 | 0.499051 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.389266 | 7.891793 | 8.318376 | 0.474359 | 0.406697 | 0.500000 | 0.452874 | 0.079060 | 0.166667 | 689.179268 | 731.837607 | 0.156008 |
| 2 | 3 | 0.030054 | 0.367294 | 7.038626 | 7.891793 | 0.423077 | 0.378562 | 0.474359 | 0.428104 | 0.070513 | 0.237179 | 603.862590 | 689.179268 | 0.220372 |
| 3 | 4 | 0.040072 | 0.347971 | 6.398751 | 7.518532 | 0.384615 | 0.357834 | 0.451923 | 0.410536 | 0.064103 | 0.301282 | 539.875082 | 651.853222 | 0.277915 |
| 4 | 5 | 0.050090 | 0.322129 | 5.758876 | 7.166601 | 0.346154 | 0.336530 | 0.430769 | 0.395735 | 0.057692 | 0.358974 | 475.887574 | 616.660092 | 0.328638 |
| 5 | 6 | 0.100051 | 0.064068 | 2.822688 | 4.997433 | 0.169666 | 0.152452 | 0.300385 | 0.274250 | 0.141026 | 0.500000 | 182.268802 | 399.743261 | 0.425526 |
| 6 | 7 | 0.150013 | 0.052095 | 0.940896 | 3.646411 | 0.056555 | 0.056791 | 0.219178 | 0.201825 | 0.047009 | 0.547009 | -5.910399 | 264.641143 | 0.422384 |
| 7 | 8 | 0.200103 | 0.047947 | 1.194433 | 3.032630 | 0.071795 | 0.049785 | 0.182285 | 0.163766 | 0.059829 | 0.606838 | 19.443349 | 203.263004 | 0.432746 |
| 8 | 9 | 0.300026 | 0.043414 | 0.812592 | 2.293251 | 0.048843 | 0.045414 | 0.137842 | 0.124349 | 0.081197 | 0.688034 | -18.740799 | 129.325094 | 0.412822 |
| 9 | 10 | 0.400077 | 0.039987 | 0.555270 | 1.858616 | 0.033376 | 0.041639 | 0.111717 | 0.103665 | 0.055556 | 0.743590 | -44.472971 | 85.861629 | 0.365481 |
| 10 | 11 | 0.500000 | 0.037264 | 0.662904 | 1.619658 | 0.039846 | 0.038618 | 0.097354 | 0.090666 | 0.066239 | 0.809829 | -33.709599 | 61.965812 | 0.329643 |
| 11 | 12 | 0.600051 | 0.034679 | 0.640696 | 1.456428 | 0.038511 | 0.035952 | 0.087543 | 0.081543 | 0.064103 | 0.873932 | -35.930351 | 45.642800 | 0.291395 |
| 12 | 13 | 0.699974 | 0.032184 | 0.299376 | 1.291256 | 0.017995 | 0.033434 | 0.077615 | 0.074675 | 0.029915 | 0.903846 | -70.062400 | 29.125618 | 0.216910 |
| 13 | 14 | 0.800026 | 0.029134 | 0.491201 | 1.191201 | 0.029525 | 0.030746 | 0.071601 | 0.069181 | 0.049145 | 0.952991 | -50.879936 | 19.120107 | 0.162748 |
| 14 | 15 | 0.899949 | 0.024977 | 0.256608 | 1.087431 | 0.015424 | 0.027250 | 0.065363 | 0.064526 | 0.025641 | 0.978632 | -74.339200 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.001434 | 0.213565 | 1.000000 | 0.012837 | 0.020371 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04863554093812048 RMSE: 0.22053467060333276 LogLoss: 0.18762977916044096 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311447 Residual deviance: 730.6303600507571 AIC: 760.6303600507571 AUC: 0.7557353696697959 AUCPR: 0.25983247103746177 Gini: 0.5114707393395919 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.12744801518733176:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1726.0 | 104.0 | 0.0568 | (104.0/1830.0) |
| 1 | 1 | 60.0 | 57.0 | 0.5128 | (60.0/117.0) |
| 2 | Total | 1786.0 | 161.0 | 0.0842 | (164.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.127448 | 0.410072 | 131.0 |
| 1 | max f2 | 0.107602 | 0.457413 | 136.0 |
| 2 | max f0point5 | 0.334831 | 0.400372 | 81.0 |
| 3 | max accuracy | 0.559235 | 0.940421 | 2.0 |
| 4 | max precision | 0.559235 | 0.666667 | 2.0 |
| 5 | max recall | 0.020540 | 1.000000 | 375.0 |
| 6 | max specificity | 0.757475 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.107602 | 0.371643 | 136.0 |
| 8 | max min_per_class_accuracy | 0.043167 | 0.685792 | 245.0 |
| 9 | max mean_per_class_accuracy | 0.063755 | 0.721241 | 171.0 |
| 10 | max tns | 0.757475 | 1829.000000 | 0.0 |
| 11 | max fns | 0.757475 | 117.000000 | 0.0 |
| 12 | max fps | 0.001480 | 1830.000000 | 399.0 |
| 13 | max tps | 0.020540 | 117.000000 | 375.0 |
| 14 | max tnr | 0.757475 | 0.999454 | 0.0 |
| 15 | max fnr | 0.757475 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001480 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020540 | 1.000000 | 375.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.46 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.442849 | 8.320513 | 8.320513 | 0.500000 | 0.507407 | 0.500000 | 0.507407 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.409071 | 6.130904 | 7.253780 | 0.368421 | 0.426272 | 0.435897 | 0.467880 | 0.059829 | 0.145299 | 513.090418 | 625.378041 | 0.133277 |
| 2 | 3 | 0.030303 | 0.381600 | 5.824359 | 6.769231 | 0.350000 | 0.393616 | 0.406780 | 0.442705 | 0.059829 | 0.205128 | 482.435897 | 576.923077 | 0.186003 |
| 3 | 4 | 0.040062 | 0.358113 | 5.255061 | 6.400394 | 0.315789 | 0.371314 | 0.384615 | 0.425315 | 0.051282 | 0.256410 | 425.506073 | 540.039448 | 0.230181 |
| 4 | 5 | 0.050334 | 0.338401 | 6.656410 | 6.452643 | 0.400000 | 0.348966 | 0.387755 | 0.409734 | 0.068376 | 0.324786 | 565.641026 | 545.264260 | 0.291999 |
| 5 | 6 | 0.100154 | 0.072405 | 3.602696 | 5.034977 | 0.216495 | 0.216049 | 0.302564 | 0.313388 | 0.179487 | 0.504274 | 260.269627 | 403.497699 | 0.429957 |
| 6 | 7 | 0.149974 | 0.055035 | 0.514671 | 3.533368 | 0.030928 | 0.060762 | 0.212329 | 0.229468 | 0.025641 | 0.529915 | -48.532910 | 253.336846 | 0.404231 |
| 7 | 8 | 0.200308 | 0.048899 | 0.679226 | 2.816174 | 0.040816 | 0.051640 | 0.169231 | 0.184783 | 0.034188 | 0.564103 | -32.077446 | 181.617357 | 0.387053 |
| 8 | 9 | 0.299949 | 0.044297 | 0.943563 | 2.194108 | 0.056701 | 0.046349 | 0.131849 | 0.138796 | 0.094017 | 0.658120 | -5.643669 | 119.410783 | 0.381070 |
| 9 | 10 | 0.400103 | 0.040806 | 0.682709 | 1.815773 | 0.041026 | 0.042514 | 0.109114 | 0.114695 | 0.068376 | 0.726496 | -31.729126 | 81.577302 | 0.347261 |
| 10 | 11 | 0.500257 | 0.037448 | 0.512032 | 1.554757 | 0.030769 | 0.038946 | 0.093429 | 0.099529 | 0.051282 | 0.777778 | -48.796844 | 55.475702 | 0.295264 |
| 11 | 12 | 0.599897 | 0.034664 | 0.343114 | 1.353508 | 0.020619 | 0.036082 | 0.081336 | 0.088991 | 0.034188 | 0.811966 | -65.688607 | 35.350808 | 0.225627 |
| 12 | 13 | 0.700051 | 0.032013 | 0.512032 | 1.233121 | 0.030769 | 0.033285 | 0.074101 | 0.081021 | 0.051282 | 0.863248 | -48.796844 | 23.312076 | 0.173630 |
| 13 | 14 | 0.799692 | 0.029163 | 0.686228 | 1.164979 | 0.041237 | 0.030612 | 0.070006 | 0.074740 | 0.068376 | 0.931624 | -31.377214 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.025559 | 0.597370 | 1.101803 | 0.035897 | 0.027579 | 0.066210 | 0.069491 | 0.059829 | 0.991453 | -40.262985 | 10.180307 | 0.097464 |
| 15 | 16 | 1.000000 | 0.001363 | 0.085339 | 1.000000 | 0.005128 | 0.020955 | 0.060092 | 0.064630 | 0.008547 | 1.000000 | -91.466141 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04827688690544305 RMSE: 0.21972001935518543 LogLoss: 0.18715332633285844 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.2667597787677 Residual deviance: 2914.3515976552712 AIC: 2944.3515976552712 AUC: 0.7677280642742518 AUCPR: 0.28578312482868484 Gini: 0.5354561285485036 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.14165134396881102:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6988.0 | 330.0 | 0.0451 | (330.0/7318.0) |
| 1 | 1 | 264.0 | 204.0 | 0.5641 | (264.0/468.0) |
| 2 | Total | 7252.0 | 534.0 | 0.0763 | (594.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.141651 | 0.407186 | 173.0 |
| 1 | max f2 | 0.066583 | 0.437357 | 220.0 |
| 2 | max f0point5 | 0.327482 | 0.413306 | 111.0 |
| 3 | max accuracy | 0.454755 | 0.940920 | 30.0 |
| 4 | max precision | 0.557729 | 0.692308 | 10.0 |
| 5 | max recall | 0.018413 | 1.000000 | 383.0 |
| 6 | max specificity | 0.856881 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.141651 | 0.367517 | 173.0 |
| 8 | max min_per_class_accuracy | 0.042353 | 0.692402 | 283.0 |
| 9 | max mean_per_class_accuracy | 0.062664 | 0.710850 | 226.0 |
| 10 | max tns | 0.856881 | 7317.000000 | 0.0 |
| 11 | max fns | 0.856881 | 468.000000 | 0.0 |
| 12 | max fps | 0.001791 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018413 | 468.000000 | 383.0 |
| 14 | max tnr | 0.856881 | 0.999863 | 0.0 |
| 15 | max fnr | 0.856881 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001791 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018413 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.425315 | 8.318376 | 8.318376 | 0.500000 | 0.499043 | 0.500000 | 0.499043 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.389508 | 8.318376 | 8.318376 | 0.500000 | 0.406072 | 0.500000 | 0.452557 | 0.083333 | 0.166667 | 731.837607 | 731.837607 | 0.156008 |
| 2 | 3 | 0.030054 | 0.367054 | 5.972167 | 7.536307 | 0.358974 | 0.377835 | 0.452991 | 0.427650 | 0.059829 | 0.226496 | 497.216743 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.347190 | 6.185459 | 7.198595 | 0.371795 | 0.357578 | 0.432692 | 0.410132 | 0.061966 | 0.288462 | 518.545913 | 619.859467 | 0.264275 |
| 4 | 5 | 0.050090 | 0.324641 | 6.612043 | 7.081284 | 0.397436 | 0.336570 | 0.425641 | 0.395420 | 0.066239 | 0.354701 | 561.204252 | 608.128424 | 0.324091 |
| 5 | 6 | 0.100051 | 0.063549 | 2.779920 | 4.933363 | 0.167095 | 0.152550 | 0.296534 | 0.274141 | 0.138889 | 0.493590 | 177.992002 | 393.336296 | 0.418706 |
| 6 | 7 | 0.150013 | 0.052001 | 0.855360 | 3.575192 | 0.051414 | 0.056838 | 0.214897 | 0.201769 | 0.042735 | 0.536325 | -14.463999 | 257.519245 | 0.411017 |
| 7 | 8 | 0.200103 | 0.047848 | 1.151775 | 2.968560 | 0.069231 | 0.049678 | 0.178434 | 0.163697 | 0.057692 | 0.594017 | 15.177515 | 196.856039 | 0.419106 |
| 8 | 9 | 0.300026 | 0.043442 | 0.727056 | 2.222032 | 0.043702 | 0.045421 | 0.133562 | 0.124305 | 0.072650 | 0.666667 | -27.294399 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.040142 | 0.640696 | 1.826571 | 0.038511 | 0.041703 | 0.109791 | 0.103648 | 0.064103 | 0.730769 | -35.930351 | 82.657118 | 0.351841 |
| 10 | 11 | 0.500000 | 0.037372 | 0.598752 | 1.581197 | 0.035990 | 0.038692 | 0.095042 | 0.090667 | 0.059829 | 0.790598 | -40.124800 | 58.119658 | 0.309183 |
| 11 | 12 | 0.600051 | 0.034757 | 0.662053 | 1.427940 | 0.039795 | 0.036063 | 0.085830 | 0.081562 | 0.066239 | 0.856838 | -33.794696 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.032269 | 0.449064 | 1.288204 | 0.026992 | 0.033505 | 0.077431 | 0.074702 | 0.044872 | 0.901709 | -55.093600 | 28.820356 | 0.214636 |
| 13 | 14 | 0.800026 | 0.029239 | 0.384418 | 1.175176 | 0.023107 | 0.030809 | 0.070637 | 0.069213 | 0.038462 | 0.940171 | -61.558211 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.025181 | 0.320760 | 1.080309 | 0.019280 | 0.027347 | 0.064935 | 0.064564 | 0.032051 | 0.972222 | -67.924000 | 8.030858 | 0.076896 |
| 15 | 16 | 1.000000 | 0.001584 | 0.277635 | 1.000000 | 0.016688 | 0.020435 | 0.060108 | 0.060149 | 0.027778 | 1.000000 | -72.236486 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9356653 | 0.021291055 | 0.93846154 | 0.9269231 | 0.96153843 | 0.93846154 | 0.9576923 | 0.9076923 | 0.8769231 | 0.9576923 | 0.9346154 | 0.90384614 | 0.90384614 | 0.9153846 | 0.9576923 | 0.9653846 | 0.9153846 | 0.91923076 | 0.93436295 | 0.9459459 | 0.94208497 | 0.93436295 | 0.9498069 | 0.93822396 | 0.93436295 | 0.9498069 | 0.9498069 | 0.95366794 | 0.9459459 | 0.90733594 | 0.94208497 | 0.96138996 |
| 1 | auc | 0.7775007 | 0.07075508 | 0.8506415 | 0.75281763 | 0.8176184 | 0.72771513 | 0.65986395 | 0.7763065 | 0.6786395 | 0.8514785 | 0.72089976 | 0.83210886 | 0.6385674 | 0.8439583 | 0.7479839 | 0.8170865 | 0.7520492 | 0.8511565 | 0.8087432 | 0.84467673 | 0.63442624 | 0.72605735 | 0.731648 | 0.768306 | 0.77339786 | 0.9281525 | 0.83527696 | 0.75797325 | 0.8704424 | 0.75350875 | 0.76413995 | 0.8093792 |
| 2 | err | 0.06433472 | 0.021291055 | 0.06153846 | 0.073076926 | 0.03846154 | 0.06153846 | 0.042307694 | 0.092307694 | 0.12307692 | 0.042307694 | 0.06538462 | 0.09615385 | 0.09615385 | 0.08461539 | 0.042307694 | 0.034615386 | 0.08461539 | 0.08076923 | 0.06563707 | 0.054054055 | 0.057915058 | 0.06563707 | 0.05019305 | 0.06177606 | 0.06563707 | 0.05019305 | 0.05019305 | 0.046332046 | 0.054054055 | 0.09266409 | 0.057915058 | 0.038610037 |
| 3 | err_count | 16.7 | 5.540758 | 16.0 | 19.0 | 10.0 | 16.0 | 11.0 | 24.0 | 32.0 | 11.0 | 17.0 | 25.0 | 25.0 | 22.0 | 11.0 | 9.0 | 22.0 | 21.0 | 17.0 | 14.0 | 15.0 | 17.0 | 13.0 | 16.0 | 17.0 | 13.0 | 13.0 | 12.0 | 14.0 | 24.0 | 15.0 | 10.0 |
| 4 | f0point5 | 0.48226658 | 0.09256819 | 0.4918033 | 0.46296296 | 0.44444445 | 0.4861111 | 0.6451613 | 0.49107143 | 0.25179857 | 0.53571427 | 0.5084746 | 0.29126215 | 0.32967034 | 0.48387095 | 0.46875 | 0.5813953 | 0.4017857 | 0.4054054 | 0.4597701 | 0.5882353 | 0.45454547 | 0.4385965 | 0.6122449 | 0.46666667 | 0.5 | 0.48192772 | 0.5487805 | 0.625 | 0.58441556 | 0.4054054 | 0.45454547 | 0.5681818 |
| 5 | f1 | 0.45874995 | 0.074219115 | 0.42857143 | 0.51282054 | 0.44444445 | 0.46666667 | 0.42105263 | 0.47826087 | 0.3043478 | 0.5217391 | 0.41379312 | 0.3243243 | 0.3243243 | 0.5217391 | 0.3529412 | 0.5263158 | 0.45 | 0.46153846 | 0.4848485 | 0.5882353 | 0.4 | 0.37037036 | 0.48 | 0.46666667 | 0.4516129 | 0.55172414 | 0.58064514 | 0.5 | 0.5625 | 0.42857143 | 0.44444445 | 0.5 |
| 6 | f2 | 0.45127094 | 0.09542558 | 0.37974682 | 0.57471263 | 0.44444445 | 0.44871795 | 0.3125 | 0.4661017 | 0.3846154 | 0.5084746 | 0.3488372 | 0.36585367 | 0.31914893 | 0.5660377 | 0.28301886 | 0.48076922 | 0.5113636 | 0.53571427 | 0.51282054 | 0.5882353 | 0.35714287 | 0.32051283 | 0.39473686 | 0.46666667 | 0.4117647 | 0.6451613 | 0.6164383 | 0.41666666 | 0.5421687 | 0.45454547 | 0.4347826 | 0.44642857 |
| 7 | lift_top_group | 9.794138 | 5.161378 | 10.196078 | 5.4166665 | 9.62963 | 10.833333 | 17.333334 | 7.2222223 | 0.0 | 7.2222223 | 13.684211 | 11.555555 | 4.5614033 | 8.666667 | 14.444445 | 15.757576 | 10.833333 | 5.7777777 | 11.511111 | 5.0784316 | 5.7555556 | 15.235294 | 15.235294 | 11.511111 | 4.796296 | 0.0 | 18.5 | 16.1875 | 10.156863 | 0.0 | 12.333333 | 14.388889 |
| 8 | logloss | 0.18565243 | 0.03371474 | 0.19111966 | 0.18490024 | 0.12173862 | 0.19214779 | 0.19450355 | 0.27388144 | 0.20731789 | 0.14584818 | 0.23114963 | 0.19127616 | 0.24412675 | 0.20926754 | 0.16977482 | 0.14481387 | 0.19501305 | 0.17412509 | 0.17331597 | 0.17332731 | 0.19826676 | 0.2115697 | 0.19827975 | 0.17813051 | 0.20494789 | 0.12750414 | 0.14431208 | 0.18654458 | 0.16886178 | 0.22165489 | 0.17191389 | 0.13993973 |
| 9 | max_per_class_error | 0.5466281 | 0.12006962 | 0.64705884 | 0.375 | 0.5555556 | 0.5625 | 0.73333335 | 0.5416667 | 0.53333336 | 0.5 | 0.68421054 | 0.6 | 0.68421054 | 0.4 | 0.75 | 0.54545456 | 0.4375 | 0.4 | 0.46666667 | 0.4117647 | 0.6666667 | 0.7058824 | 0.64705884 | 0.53333336 | 0.6111111 | 0.27272728 | 0.35714287 | 0.625 | 0.47058824 | 0.5263158 | 0.5714286 | 0.5833333 |
| 10 | mcc | 0.4384124 | 0.079631574 | 0.4081755 | 0.48383936 | 0.42452413 | 0.43525764 | 0.5051815 | 0.42822015 | 0.2652846 | 0.5001678 | 0.4049163 | 0.2803991 | 0.27272066 | 0.48112524 | 0.36962467 | 0.5157763 | 0.41596073 | 0.43398517 | 0.45224053 | 0.55930966 | 0.37928465 | 0.35149625 | 0.49335808 | 0.4338798 | 0.4239339 | 0.54475677 | 0.55717295 | 0.51035756 | 0.5350011 | 0.3806954 | 0.41425505 | 0.49147138 |
| 11 | mean_per_class_accuracy | 0.70998436 | 0.055601012 | 0.6661825 | 0.78586066 | 0.71226203 | 0.7044057 | 0.6333333 | 0.70586157 | 0.68435377 | 0.73991936 | 0.649596 | 0.66734695 | 0.63299847 | 0.7708333 | 0.62096775 | 0.7212486 | 0.7505123 | 0.7693878 | 0.7461749 | 0.77965486 | 0.65642077 | 0.6367282 | 0.67233837 | 0.71693987 | 0.6819963 | 0.84347504 | 0.80510205 | 0.6833848 | 0.7523092 | 0.70767546 | 0.7 | 0.70226043 |
| 12 | mean_per_class_error | 0.29001567 | 0.055601012 | 0.33381748 | 0.21413934 | 0.28773794 | 0.29559427 | 0.36666667 | 0.29413843 | 0.31564626 | 0.26008064 | 0.35040402 | 0.33265308 | 0.36700153 | 0.22916667 | 0.37903225 | 0.27875137 | 0.2494877 | 0.23061225 | 0.25382513 | 0.22034517 | 0.34357923 | 0.36327174 | 0.32766163 | 0.2830601 | 0.31800368 | 0.15652493 | 0.19489796 | 0.31661522 | 0.24769081 | 0.29232457 | 0.3 | 0.29773954 |
| 13 | mse | 0.047911387 | 0.009755971 | 0.0507161 | 0.04742206 | 0.028791862 | 0.048268337 | 0.047806997 | 0.07472736 | 0.052779734 | 0.03730585 | 0.05905863 | 0.050103296 | 0.063820295 | 0.057573915 | 0.042062882 | 0.034842182 | 0.051142145 | 0.04654345 | 0.04458453 | 0.04618426 | 0.04931379 | 0.054269318 | 0.050693132 | 0.045852102 | 0.0543221 | 0.032692343 | 0.036276583 | 0.047550105 | 0.045077868 | 0.05986229 | 0.043672647 | 0.034025494 |
| 14 | null_deviance | 118.00889 | 16.891634 | 125.73113 | 120.223526 | 81.936905 | 120.223526 | 114.7255 | 164.55519 | 114.7255 | 98.28862 | 136.7752 | 114.7255 | 136.7752 | 142.31174 | 98.28862 | 92.82863 | 120.223526 | 114.7255 | 114.60118 | 125.607956 | 114.60118 | 125.607956 | 125.607956 | 114.60118 | 131.12575 | 92.701996 | 109.11213 | 120.09978 | 125.607956 | 136.65318 | 109.11213 | 98.16257 |
| 15 | pr_auc | 0.3294095 | 0.087964155 | 0.4049724 | 0.3584581 | 0.31763986 | 0.3674188 | 0.347388 | 0.37520036 | 0.13521223 | 0.30192876 | 0.36053383 | 0.2341406 | 0.1825808 | 0.38277313 | 0.21549378 | 0.4269294 | 0.25595897 | 0.28396782 | 0.35303065 | 0.39219552 | 0.2150982 | 0.34143946 | 0.42974272 | 0.29285216 | 0.3222945 | 0.27143982 | 0.5030655 | 0.44175854 | 0.44484377 | 0.21832886 | 0.2860632 | 0.4195353 |
| 16 | precision | 0.5142683 | 0.15082124 | 0.54545456 | 0.4347826 | 0.44444445 | 0.5 | 1.0 | 0.5 | 0.22580644 | 0.54545456 | 0.6 | 0.27272728 | 0.33333334 | 0.46153846 | 0.6 | 0.625 | 0.375 | 0.375 | 0.44444445 | 0.5882353 | 0.5 | 0.5 | 0.75 | 0.46666667 | 0.53846157 | 0.44444445 | 0.5294118 | 0.75 | 0.6 | 0.39130434 | 0.46153846 | 0.625 |
| 17 | r2 | 0.15063101 | 0.0603448 | 0.1700779 | 0.17885986 | 0.13841088 | 0.16420603 | 0.12061144 | 0.108126864 | 0.029140128 | 0.15259564 | 0.12811458 | 0.07837202 | 0.05781784 | 0.18916737 | 0.0445394 | 0.14007616 | 0.11444441 | 0.14385381 | 0.18284835 | 0.24694063 | 0.0961698 | 0.115109384 | 0.17342104 | 0.15961613 | 0.15998599 | 0.19610153 | 0.29053372 | 0.17960168 | 0.2649809 | 0.119381085 | 0.1458878 | 0.22993782 |
| 18 | recall | 0.4533719 | 0.12006962 | 0.3529412 | 0.625 | 0.44444445 | 0.4375 | 0.26666668 | 0.45833334 | 0.46666667 | 0.5 | 0.31578946 | 0.4 | 0.31578946 | 0.6 | 0.25 | 0.45454547 | 0.5625 | 0.6 | 0.53333336 | 0.5882353 | 0.33333334 | 0.29411766 | 0.3529412 | 0.46666667 | 0.3888889 | 0.72727275 | 0.64285713 | 0.375 | 0.5294118 | 0.47368422 | 0.42857143 | 0.41666666 |
| 19 | residual_deviance | 96.372696 | 17.548994 | 99.38222 | 96.148125 | 63.30408 | 99.916855 | 101.14184 | 142.41835 | 107.805305 | 75.84106 | 120.19781 | 99.46361 | 126.94591 | 108.81912 | 88.282906 | 75.30321 | 101.406784 | 90.54505 | 89.77767 | 89.783554 | 102.70218 | 109.5931 | 102.708916 | 92.27161 | 106.16301 | 66.04715 | 74.753654 | 96.6301 | 87.470406 | 114.81724 | 89.0514 | 72.48878 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:11:38 | 0.000 sec | 2 | .87E1 | 15 | 0.452124 | 0.451962 | 0.452389 | 0.011829 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:11:38 | 0.003 sec | 4 | .54E1 | 15 | 0.450690 | 0.450483 | 0.451014 | 0.011806 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:11:38 | 0.005 sec | 6 | .34E1 | 15 | 0.448435 | 0.448160 | 0.448850 | 0.011771 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:11:38 | 0.007 sec | 8 | .21E1 | 15 | 0.444926 | 0.444552 | 0.445478 | 0.011719 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:11:38 | 0.010 sec | 10 | .13E1 | 15 | 0.439629 | 0.439117 | 0.440376 | 0.011647 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:11:38 | 0.013 sec | 12 | .81E0 | 15 | 0.431976 | 0.431303 | 0.432978 | 0.011555 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:11:38 | 0.015 sec | 14 | .5E0 | 15 | 0.421745 | 0.420933 | 0.423021 | 0.011462 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:11:38 | 0.018 sec | 16 | .31E0 | 15 | 0.409723 | 0.408896 | 0.411208 | 0.011405 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:11:38 | 0.021 sec | 18 | .19E0 | 15 | 0.397882 | 0.397278 | 0.399445 | 0.011431 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:11:38 | 0.023 sec | 20 | .12E0 | 15 | 0.388239 | 0.388118 | 0.389810 | 0.011547 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:11:38 | 0.025 sec | 22 | .75E-1 | 15 | 0.381487 | 0.382026 | 0.383098 | 0.011720 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:11:38 | 0.028 sec | 24 | .46E-1 | 15 | 0.377176 | 0.378452 | 0.378907 | 0.011906 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:11:38 | 0.030 sec | 26 | .29E-1 | 15 | 0.374529 | 0.376545 | 0.376449 | 0.012075 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:11:38 | 0.033 sec | 28 | .18E-1 | 15 | 0.372904 | 0.375625 | 0.375050 | 0.012217 | 0.0 | 28.0 | 0.219137 | 0.185953 | 0.149992 | 0.778031 | 0.296741 | 8.744959 | 0.073337 | 0.220535 | 0.18763 | 0.13891 | 0.755735 | 0.259832 | 8.320513 | 0.084232 | |
| 14 | 2021-07-15 20:11:38 | 0.036 sec | 30 | .11E-1 | 15 | 0.371905 | 0.375260 | 0.374280 | 0.012330 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:11:38 | 0.039 sec | 32 | .69E-2 | 15 | 0.371299 | 0.375200 | 0.374397 | 0.012503 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:11:38 | 0.041 sec | 34 | .43E-2 | 15 | 0.370947 | 0.375291 | 0.375144 | 0.012921 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:11:38 | 0.044 sec | 36 | .27E-2 | 15 | 0.370754 | 0.375432 | 0.376556 | 0.013128 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.555506 | 1.000000 | 0.281307 |
| 1 | Average_Transaction_Frequency | 0.242767 | 0.437020 | 0.122937 |
| 2 | Merchant_ID | 0.211580 | 0.380878 | 0.107144 |
| 3 | Minimum_Transaction_Amount | 0.173858 | 0.312971 | 0.088041 |
| 4 | Channel_ID | 0.157450 | 0.283435 | 0.079732 |
| 5 | Card_Type.1 | 0.134849 | 0.242750 | 0.068287 |
| 6 | Card_Type.0 | 0.133503 | 0.240326 | 0.067606 |
| 7 | Transaction_Amount | 0.096148 | 0.173081 | 0.048689 |
| 8 | Transaction_Date | 0.076108 | 0.137007 | 0.038541 |
| 9 | Maximum_Transaction_Amount | 0.071079 | 0.127953 | 0.035994 |
| 10 | Month | 0.057478 | 0.103470 | 0.029107 |
| 11 | Day | 0.037323 | 0.067187 | 0.018900 |
| 12 | Average_Transaction_Amount | 0.023157 | 0.041686 | 0.011727 |
| 13 | City_ID | 0.003925 | 0.007066 | 0.001988 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201140 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.007009 ) | nlambda = 30, lambda.max = 8.8943, lambda.min = 0.007009, lambda.1... | 14 | 14 | 32 | automl_training_py_99_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04768820347565437 RMSE: 0.21837628872122167 LogLoss: 0.18448919328237842 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938293 Residual deviance: 2872.865717793197 AIC: 2902.865717793197 AUC: 0.7775253560474933 AUCPR: 0.29918986508765916 Gini: 0.5550507120949866 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.17305077140737277:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6988.0 | 330.0 | 0.0451 | (330.0/7318.0) |
| 1 | 1 | 257.0 | 211.0 | 0.5491 | (257.0/468.0) |
| 2 | Total | 7245.0 | 541.0 | 0.0754 | (587.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.173051 | 0.418236 | 176.0 |
| 1 | max f2 | 0.063156 | 0.452489 | 228.0 |
| 2 | max f0point5 | 0.331408 | 0.427820 | 119.0 |
| 3 | max accuracy | 0.446613 | 0.941305 | 43.0 |
| 4 | max precision | 0.842347 | 1.000000 | 0.0 |
| 5 | max recall | 0.016707 | 1.000000 | 386.0 |
| 6 | max specificity | 0.842347 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.271062 | 0.379737 | 151.0 |
| 8 | max min_per_class_accuracy | 0.041221 | 0.699645 | 288.0 |
| 9 | max mean_per_class_accuracy | 0.047016 | 0.722106 | 265.0 |
| 10 | max tns | 0.842347 | 7318.000000 | 0.0 |
| 11 | max fns | 0.842347 | 467.000000 | 0.0 |
| 12 | max fps | 0.001696 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016707 | 468.000000 | 386.0 |
| 14 | max tnr | 0.842347 | 1.000000 | 0.0 |
| 15 | max fnr | 0.842347 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001696 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016707 | 1.000000 | 386.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.446371 | 9.598126 | 9.598126 | 0.576923 | 0.512395 | 0.576923 | 0.512395 | 0.096154 | 0.096154 | 859.812623 | 859.812623 | 0.091644 |
| 1 | 2 | 0.020036 | 0.409061 | 6.185459 | 7.891793 | 0.371795 | 0.426280 | 0.474359 | 0.469337 | 0.061966 | 0.158120 | 518.545913 | 689.179268 | 0.146914 |
| 2 | 3 | 0.030054 | 0.381499 | 7.038626 | 7.607404 | 0.423077 | 0.395706 | 0.457265 | 0.444794 | 0.070513 | 0.228632 | 603.862590 | 660.740375 | 0.211278 |
| 3 | 4 | 0.040072 | 0.361907 | 6.612043 | 7.358563 | 0.397436 | 0.371822 | 0.442308 | 0.426551 | 0.066239 | 0.294872 | 561.204252 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.337774 | 6.612043 | 7.209259 | 0.397436 | 0.350630 | 0.433333 | 0.411367 | 0.066239 | 0.361111 | 561.204252 | 620.925926 | 0.330912 |
| 5 | 6 | 0.100051 | 0.062933 | 2.993760 | 5.104215 | 0.179949 | 0.162507 | 0.306804 | 0.287096 | 0.149573 | 0.510684 | 199.376002 | 410.421535 | 0.436893 |
| 6 | 7 | 0.150013 | 0.051034 | 0.812592 | 3.674899 | 0.048843 | 0.055658 | 0.220890 | 0.210016 | 0.040598 | 0.551282 | -18.740799 | 267.489902 | 0.426931 |
| 7 | 8 | 0.200103 | 0.046660 | 1.279750 | 3.075343 | 0.076923 | 0.048592 | 0.184852 | 0.169608 | 0.064103 | 0.615385 | 27.975016 | 207.534314 | 0.441840 |
| 8 | 9 | 0.300026 | 0.041953 | 0.748440 | 2.300373 | 0.044987 | 0.044087 | 0.138271 | 0.127804 | 0.074786 | 0.690171 | -25.155999 | 130.037283 | 0.415096 |
| 9 | 10 | 0.400077 | 0.038639 | 0.512557 | 1.853275 | 0.030809 | 0.040200 | 0.111396 | 0.105896 | 0.051282 | 0.741453 | -48.744281 | 85.327544 | 0.363208 |
| 10 | 11 | 0.500000 | 0.035906 | 0.684288 | 1.619658 | 0.041131 | 0.037220 | 0.097354 | 0.092171 | 0.068376 | 0.809829 | -31.571199 | 61.965812 | 0.329643 |
| 11 | 12 | 0.600051 | 0.033342 | 0.448488 | 1.424379 | 0.026958 | 0.034562 | 0.085616 | 0.082565 | 0.044872 | 0.854701 | -55.151246 | 42.437946 | 0.270935 |
| 12 | 13 | 0.699974 | 0.030667 | 0.491832 | 1.291256 | 0.029563 | 0.031997 | 0.077615 | 0.075347 | 0.049145 | 0.903846 | -50.816800 | 29.125618 | 0.216910 |
| 13 | 14 | 0.800026 | 0.027562 | 0.384418 | 1.177847 | 0.023107 | 0.029194 | 0.070798 | 0.069575 | 0.038462 | 0.942308 | -61.558211 | 17.784680 | 0.151381 |
| 14 | 15 | 0.899949 | 0.023308 | 0.363528 | 1.087431 | 0.021851 | 0.025591 | 0.065363 | 0.064691 | 0.036325 | 0.978632 | -63.647200 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.001375 | 0.213565 | 1.000000 | 0.012837 | 0.018882 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04978011781401978 RMSE: 0.2231145844942006 LogLoss: 0.19292613574205436 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311415 Residual deviance: 751.2543725795599 AIC: 781.2543725795599 AUC: 0.7448227546588203 AUCPR: 0.25167272158727744 Gini: 0.4896455093176406 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.11017185775222935:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1730.0 | 100.0 | 0.0546 | (100.0/1830.0) |
| 1 | 1 | 66.0 | 51.0 | 0.5641 | (66.0/117.0) |
| 2 | Total | 1796.0 | 151.0 | 0.0853 | (166.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.110172 | 0.380597 | 130.0 |
| 1 | max f2 | 0.060386 | 0.414244 | 172.0 |
| 2 | max f0point5 | 0.331218 | 0.387524 | 85.0 |
| 3 | max accuracy | 0.816082 | 0.940421 | 0.0 |
| 4 | max precision | 0.816082 | 1.000000 | 0.0 |
| 5 | max recall | 0.019333 | 1.000000 | 376.0 |
| 6 | max specificity | 0.816082 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.110172 | 0.338758 | 130.0 |
| 8 | max min_per_class_accuracy | 0.040971 | 0.666667 | 247.0 |
| 9 | max mean_per_class_accuracy | 0.060386 | 0.699054 | 172.0 |
| 10 | max tns | 0.816082 | 1830.000000 | 0.0 |
| 11 | max fns | 0.816082 | 116.000000 | 0.0 |
| 12 | max fps | 0.001829 | 1830.000000 | 399.0 |
| 13 | max tps | 0.019333 | 117.000000 | 376.0 |
| 14 | max tnr | 0.816082 | 1.000000 | 0.0 |
| 15 | max fnr | 0.816082 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001829 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019333 | 1.000000 | 376.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.17 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.434398 | 6.656410 | 6.656410 | 0.400000 | 0.511913 | 0.400000 | 0.511913 | 0.068376 | 0.068376 | 565.641026 | 565.641026 | 0.061819 |
| 1 | 2 | 0.020031 | 0.405060 | 7.006748 | 6.827087 | 0.421053 | 0.420503 | 0.410256 | 0.467380 | 0.068376 | 0.136752 | 600.674764 | 582.708744 | 0.124184 |
| 2 | 3 | 0.030303 | 0.373990 | 6.656410 | 6.769231 | 0.400000 | 0.390676 | 0.406780 | 0.441378 | 0.068376 | 0.205128 | 565.641026 | 576.923077 | 0.186003 |
| 3 | 4 | 0.040062 | 0.357502 | 7.006748 | 6.827087 | 0.421053 | 0.366814 | 0.410256 | 0.423215 | 0.068376 | 0.273504 | 600.674764 | 582.708744 | 0.248368 |
| 4 | 5 | 0.050334 | 0.338299 | 4.160256 | 6.282836 | 0.250000 | 0.348825 | 0.377551 | 0.408034 | 0.042735 | 0.316239 | 316.025641 | 528.283621 | 0.282906 |
| 5 | 6 | 0.100154 | 0.069000 | 2.916468 | 4.608284 | 0.175258 | 0.184053 | 0.276923 | 0.296618 | 0.145299 | 0.461538 | 191.646841 | 360.828402 | 0.384489 |
| 6 | 7 | 0.149974 | 0.052655 | 0.857785 | 3.362399 | 0.051546 | 0.057815 | 0.202055 | 0.217289 | 0.042735 | 0.504274 | -14.221517 | 236.239902 | 0.376951 |
| 7 | 8 | 0.200308 | 0.047080 | 0.339613 | 2.602827 | 0.020408 | 0.049715 | 0.156410 | 0.175181 | 0.017094 | 0.521368 | -66.038723 | 160.282709 | 0.341586 |
| 8 | 9 | 0.299949 | 0.042701 | 0.943563 | 2.051633 | 0.056701 | 0.044713 | 0.123288 | 0.131841 | 0.094017 | 0.615385 | -5.643669 | 105.163330 | 0.335603 |
| 9 | 10 | 0.400103 | 0.039143 | 0.853386 | 1.751687 | 0.051282 | 0.040775 | 0.105263 | 0.109045 | 0.085470 | 0.700855 | -14.661407 | 75.168691 | 0.319980 |
| 10 | 11 | 0.500257 | 0.036408 | 0.682709 | 1.537672 | 0.041026 | 0.037765 | 0.092402 | 0.094774 | 0.068376 | 0.769231 | -31.729126 | 53.767177 | 0.286171 |
| 11 | 12 | 0.599897 | 0.033437 | 0.686228 | 1.396250 | 0.041237 | 0.034879 | 0.083904 | 0.084826 | 0.068376 | 0.837607 | -31.377214 | 39.625044 | 0.252907 |
| 12 | 13 | 0.700051 | 0.031026 | 0.597370 | 1.281957 | 0.035897 | 0.032213 | 0.077036 | 0.077299 | 0.059829 | 0.897436 | -40.262985 | 28.195722 | 0.210004 |
| 13 | 14 | 0.799692 | 0.028275 | 0.428892 | 1.175667 | 0.025773 | 0.029688 | 0.070649 | 0.071366 | 0.042735 | 0.940171 | -57.110759 | 17.566655 | 0.149461 |
| 14 | 15 | 0.899846 | 0.023945 | 0.426693 | 1.092305 | 0.025641 | 0.026326 | 0.065639 | 0.066353 | 0.042735 | 0.982906 | -57.330703 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.001829 | 0.170677 | 1.000000 | 0.010256 | 0.019636 | 0.060092 | 0.061674 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04788143558909687 RMSE: 0.2188182706930499 LogLoss: 0.1856161044570436 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.043021999563 Residual deviance: 2890.413978605083 AIC: 2920.413978605083 AUC: 0.7736257396000494 AUCPR: 0.2919593034707008 Gini: 0.5472514792000989 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.24332660139874696:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7030.0 | 288.0 | 0.0394 | (288.0/7318.0) |
| 1 | 1 | 268.0 | 200.0 | 0.5726 | (268.0/468.0) |
| 2 | Total | 7298.0 | 488.0 | 0.0714 | (556.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.243327 | 0.418410 | 155.0 |
| 1 | max f2 | 0.073480 | 0.439889 | 211.0 |
| 2 | max f0point5 | 0.326753 | 0.427184 | 121.0 |
| 3 | max accuracy | 0.451595 | 0.941305 | 42.0 |
| 4 | max precision | 0.648171 | 0.600000 | 4.0 |
| 5 | max recall | 0.016621 | 1.000000 | 384.0 |
| 6 | max specificity | 0.861195 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.243327 | 0.380482 | 155.0 |
| 8 | max min_per_class_accuracy | 0.043234 | 0.698718 | 275.0 |
| 9 | max mean_per_class_accuracy | 0.061004 | 0.710490 | 227.0 |
| 10 | max tns | 0.861195 | 7317.000000 | 0.0 |
| 11 | max fns | 0.861195 | 468.000000 | 0.0 |
| 12 | max fps | 0.001512 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016621 | 468.000000 | 384.0 |
| 14 | max tnr | 0.861195 | 0.999863 | 0.0 |
| 15 | max fnr | 0.861195 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001512 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016621 | 1.000000 | 384.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.07 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.442449 | 9.171543 | 9.171543 | 0.551282 | 0.512412 | 0.551282 | 0.512412 | 0.091880 | 0.091880 | 817.154284 | 817.154284 | 0.087098 |
| 1 | 2 | 0.020036 | 0.404722 | 7.465209 | 8.318376 | 0.448718 | 0.421907 | 0.500000 | 0.467160 | 0.074786 | 0.166667 | 646.520929 | 731.837607 | 0.156008 |
| 2 | 3 | 0.030054 | 0.378580 | 6.825334 | 7.820695 | 0.410256 | 0.392577 | 0.470085 | 0.442299 | 0.068376 | 0.235043 | 582.533421 | 682.069545 | 0.218098 |
| 3 | 4 | 0.040072 | 0.356959 | 5.545584 | 7.251918 | 0.333333 | 0.367461 | 0.435897 | 0.423589 | 0.055556 | 0.290598 | 454.558405 | 625.191760 | 0.266548 |
| 4 | 5 | 0.050090 | 0.331183 | 7.891793 | 7.379893 | 0.474359 | 0.343692 | 0.443590 | 0.407610 | 0.079060 | 0.369658 | 689.179268 | 637.989261 | 0.340005 |
| 5 | 6 | 0.100051 | 0.060731 | 2.523312 | 4.954720 | 0.151671 | 0.145538 | 0.297818 | 0.276742 | 0.126068 | 0.495726 | 152.331202 | 395.471951 | 0.420979 |
| 6 | 7 | 0.168122 | 0.060058 | 0.973093 | 3.342602 | 0.058491 | 0.060190 | 0.200917 | 0.189063 | 0.066239 | 0.561966 | -2.690695 | 234.260184 | 0.419031 |
| 7 | 8 | 0.200103 | 0.052432 | 1.002214 | 2.968560 | 0.060241 | 0.055796 | 0.178434 | 0.167764 | 0.032051 | 0.594017 | 0.221398 | 196.856039 | 0.419106 |
| 8 | 9 | 0.300026 | 0.044062 | 0.919512 | 2.286129 | 0.055270 | 0.047446 | 0.137414 | 0.127692 | 0.091880 | 0.685897 | -8.048799 | 128.612904 | 0.410549 |
| 9 | 10 | 0.400077 | 0.040149 | 0.619340 | 1.869298 | 0.037227 | 0.042025 | 0.112360 | 0.106268 | 0.061966 | 0.747863 | -38.066006 | 86.929799 | 0.370028 |
| 10 | 11 | 0.500000 | 0.036900 | 0.555984 | 1.606838 | 0.033419 | 0.038428 | 0.096584 | 0.092711 | 0.055556 | 0.803419 | -44.401600 | 60.683761 | 0.322823 |
| 11 | 12 | 0.600051 | 0.034092 | 0.512557 | 1.424379 | 0.030809 | 0.035519 | 0.085616 | 0.083175 | 0.051282 | 0.854701 | -48.744281 | 42.437946 | 0.270935 |
| 12 | 13 | 0.699974 | 0.031443 | 0.470448 | 1.288204 | 0.028278 | 0.032753 | 0.077431 | 0.075977 | 0.047009 | 0.901709 | -52.955200 | 28.820356 | 0.214636 |
| 13 | 14 | 0.800026 | 0.028153 | 0.341705 | 1.169834 | 0.020539 | 0.029846 | 0.070316 | 0.070208 | 0.034188 | 0.935897 | -65.829521 | 16.983423 | 0.144561 |
| 14 | 15 | 0.899949 | 0.023862 | 0.427680 | 1.087431 | 0.025707 | 0.026150 | 0.065363 | 0.065316 | 0.042735 | 0.978632 | -57.232000 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.001372 | 0.213565 | 1.000000 | 0.012837 | 0.019289 | 0.060108 | 0.060711 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9261499 | 0.031744823 | 0.9423077 | 0.9269231 | 0.93846154 | 0.9307692 | 0.8769231 | 0.9346154 | 0.9076923 | 0.9423077 | 0.9346154 | 0.9346154 | 0.95384616 | 0.91923076 | 0.93846154 | 0.88076925 | 0.9153846 | 0.93846154 | 0.9459459 | 0.969112 | 0.9150579 | 0.96138996 | 0.93822396 | 0.95752895 | 0.9189189 | 0.9227799 | 0.9498069 | 0.93822396 | 0.90733594 | 0.93436295 | 0.7992278 | 0.9111969 |
| 1 | auc | 0.77468777 | 0.080080464 | 0.92828107 | 0.7395833 | 0.8141064 | 0.7567204 | 0.54943645 | 0.828125 | 0.84580004 | 0.858885 | 0.80865777 | 0.71836734 | 0.7923809 | 0.7121212 | 0.7844353 | 0.74935246 | 0.704918 | 0.7827869 | 0.8330601 | 0.8410931 | 0.8180758 | 0.86510265 | 0.76748973 | 0.7519126 | 0.5469388 | 0.7428962 | 0.7891566 | 0.71088433 | 0.81599414 | 0.7920765 | 0.81584364 | 0.77615064 |
| 2 | err | 0.07385011 | 0.031744823 | 0.057692308 | 0.073076926 | 0.06153846 | 0.06923077 | 0.12307692 | 0.06538462 | 0.092307694 | 0.057692308 | 0.06538462 | 0.06538462 | 0.046153847 | 0.08076923 | 0.06153846 | 0.11923077 | 0.08461539 | 0.06153846 | 0.054054055 | 0.03088803 | 0.08494209 | 0.038610037 | 0.06177606 | 0.042471044 | 0.08108108 | 0.077220075 | 0.05019305 | 0.06177606 | 0.09266409 | 0.06563707 | 0.2007722 | 0.08880309 |
| 3 | err_count | 19.166666 | 8.229816 | 15.0 | 19.0 | 16.0 | 18.0 | 32.0 | 17.0 | 24.0 | 15.0 | 17.0 | 17.0 | 12.0 | 21.0 | 16.0 | 31.0 | 22.0 | 16.0 | 14.0 | 8.0 | 22.0 | 10.0 | 16.0 | 11.0 | 21.0 | 20.0 | 13.0 | 16.0 | 24.0 | 17.0 | 52.0 | 23.0 |
| 4 | f0point5 | 0.44664124 | 0.12774909 | 0.5188679 | 0.3125 | 0.61904764 | 0.32894737 | 0.20833333 | 0.5 | 0.34653464 | 0.47297296 | 0.48913044 | 0.3968254 | 0.5970149 | 0.42553192 | 0.5405405 | 0.3821656 | 0.3409091 | 0.5113636 | 0.5494506 | 0.6818182 | 0.38135594 | 0.5555556 | 0.5208333 | 0.6363636 | 0.1724138 | 0.4040404 | 0.42857143 | 0.6164383 | 0.34653464 | 0.4225352 | 0.24621212 | 0.44642857 |
| 5 | f1 | 0.45947686 | 0.112497464 | 0.5945946 | 0.3448276 | 0.61904764 | 0.35714287 | 0.23809524 | 0.5405405 | 0.36842105 | 0.4827586 | 0.51428574 | 0.37037036 | 0.5714286 | 0.43243244 | 0.5 | 0.43636364 | 0.3529412 | 0.5294118 | 0.5882353 | 0.6 | 0.45 | 0.5833333 | 0.5555556 | 0.56 | 0.16 | 0.44444445 | 0.48 | 0.5294118 | 0.36842105 | 0.41379312 | 0.33333334 | 0.4651163 |
| 6 | f2 | 0.48289824 | 0.11464276 | 0.6962025 | 0.3846154 | 0.61904764 | 0.390625 | 0.2777778 | 0.5882353 | 0.39325842 | 0.49295774 | 0.5421687 | 0.3472222 | 0.5479452 | 0.43956044 | 0.4651163 | 0.5084746 | 0.36585367 | 0.5487805 | 0.6329114 | 0.53571427 | 0.5487805 | 0.61403507 | 0.5952381 | 0.5 | 0.14925373 | 0.49382716 | 0.54545456 | 0.46391752 | 0.39325842 | 0.4054054 | 0.515873 | 0.4854369 |
| 7 | lift_top_group | 8.7757225 | 5.204972 | 18.571428 | 0.0 | 8.253968 | 14.444445 | 0.0 | 5.4166665 | 10.196078 | 18.571428 | 10.833333 | 11.555555 | 11.555555 | 4.814815 | 9.62963 | 4.126984 | 10.833333 | 10.833333 | 11.511111 | 14.388889 | 12.333333 | 15.69697 | 10.791667 | 5.7555556 | 0.0 | 5.7555556 | 0.0 | 8.222222 | 5.0784316 | 5.7555556 | 5.3958335 | 12.95 |
| 8 | logloss | 0.18432689 | 0.030882163 | 0.13379957 | 0.16699505 | 0.20316628 | 0.165845 | 0.22422455 | 0.16887255 | 0.20469555 | 0.16048847 | 0.17392677 | 0.19359985 | 0.1677667 | 0.2135954 | 0.19872381 | 0.23989856 | 0.20715778 | 0.17787841 | 0.15602727 | 0.14153448 | 0.17280413 | 0.12291248 | 0.1735598 | 0.16998284 | 0.20735186 | 0.18668197 | 0.13180222 | 0.23787795 | 0.20556526 | 0.18189174 | 0.21938227 | 0.22179805 |
| 9 | max_per_class_error | 0.49110737 | 0.13940406 | 0.21428572 | 0.5833333 | 0.3809524 | 0.5833333 | 0.6875 | 0.375 | 0.5882353 | 0.5 | 0.4375 | 0.6666667 | 0.46666667 | 0.5555556 | 0.5555556 | 0.42857143 | 0.625 | 0.4375 | 0.33333334 | 0.5 | 0.35714287 | 0.36363637 | 0.375 | 0.53333336 | 0.85714287 | 0.46666667 | 0.4 | 0.5714286 | 0.5882353 | 0.6 | 0.20164609 | 0.5 |
| 10 | mcc | 0.43027613 | 0.12026696 | 0.58576304 | 0.31258383 | 0.5855748 | 0.32506365 | 0.18138558 | 0.5114615 | 0.3212981 | 0.452549 | 0.48154575 | 0.338667 | 0.54873484 | 0.38915312 | 0.4719551 | 0.3874301 | 0.3084525 | 0.49759787 | 0.56421936 | 0.59763575 | 0.4315118 | 0.56539905 | 0.5265611 | 0.5508737 | 0.11899594 | 0.4108025 | 0.46507916 | 0.51476383 | 0.3210932 | 0.37932518 | 0.34464464 | 0.41816312 |
| 11 | mean_per_class_accuracy | 0.7310778 | 0.06631644 | 0.8684669 | 0.6841398 | 0.79278743 | 0.6861559 | 0.61321723 | 0.789959 | 0.67707574 | 0.73373985 | 0.7607582 | 0.65238094 | 0.7564626 | 0.69949496 | 0.7098255 | 0.7396892 | 0.66290987 | 0.76280737 | 0.8148907 | 0.7459514 | 0.7867347 | 0.80608505 | 0.7919239 | 0.7271858 | 0.55306125 | 0.7400273 | 0.7819277 | 0.7058824 | 0.6769567 | 0.68360656 | 0.80542696 | 0.72280335 |
| 12 | mean_per_class_error | 0.26892218 | 0.06631644 | 0.1315331 | 0.3158602 | 0.2072126 | 0.31384408 | 0.3867828 | 0.21004099 | 0.32292423 | 0.26626018 | 0.23924181 | 0.34761906 | 0.24353741 | 0.30050504 | 0.29017448 | 0.26031083 | 0.33709016 | 0.23719262 | 0.18510929 | 0.2540486 | 0.2132653 | 0.19391495 | 0.20807613 | 0.2728142 | 0.44693878 | 0.2599727 | 0.2180723 | 0.29411766 | 0.32304326 | 0.31639344 | 0.19457304 | 0.27719665 |
| 13 | mse | 0.04759583 | 0.008959908 | 0.034597185 | 0.041527394 | 0.05567474 | 0.040923998 | 0.056581102 | 0.043990977 | 0.0548621 | 0.04055495 | 0.045436233 | 0.048247557 | 0.04266174 | 0.055738613 | 0.052166164 | 0.06499863 | 0.053893153 | 0.046622492 | 0.040201284 | 0.03487741 | 0.044943806 | 0.029674985 | 0.045088958 | 0.04223967 | 0.050718922 | 0.047726408 | 0.03195539 | 0.06301342 | 0.05490285 | 0.047406495 | 0.057603586 | 0.05904464 |
| 14 | null_deviance | 118.001434 | 15.413841 | 109.23703 | 98.28862 | 147.85797 | 98.28862 | 120.223526 | 120.223526 | 125.73113 | 109.23703 | 120.223526 | 114.7255 | 114.7255 | 131.24835 | 131.24835 | 147.85797 | 120.223526 | 120.223526 | 114.60118 | 98.16257 | 109.11213 | 92.701996 | 120.09978 | 114.60118 | 109.11213 | 114.60118 | 87.25086 | 147.73709 | 125.607956 | 114.60118 | 120.09978 | 142.19029 |
| 15 | pr_auc | 0.3204807 | 0.114520155 | 0.5964937 | 0.1514291 | 0.39224416 | 0.20441686 | 0.09739581 | 0.44116092 | 0.33888352 | 0.4697801 | 0.44004536 | 0.2879104 | 0.39721593 | 0.23660842 | 0.35846552 | 0.29616478 | 0.2144023 | 0.31489265 | 0.41788635 | 0.3743784 | 0.30382878 | 0.44635275 | 0.33296537 | 0.36266902 | 0.07613121 | 0.3103769 | 0.22034886 | 0.36165488 | 0.2673644 | 0.31572714 | 0.18843733 | 0.39878985 |
| 16 | precision | 0.442643 | 0.14520718 | 0.47826087 | 0.29411766 | 0.61904764 | 0.3125 | 0.1923077 | 0.47619048 | 0.33333334 | 0.46666667 | 0.47368422 | 0.41666666 | 0.61538464 | 0.42105263 | 0.5714286 | 0.3529412 | 0.33333334 | 0.5 | 0.5263158 | 0.75 | 0.34615386 | 0.53846157 | 0.5 | 0.7 | 0.18181819 | 0.3809524 | 0.4 | 0.6923077 | 0.33333334 | 0.42857143 | 0.20967741 | 0.4347826 |
| 17 | r2 | 0.15555434 | 0.08121298 | 0.32091472 | 0.056703042 | 0.25012702 | 0.07040922 | 0.020265726 | 0.23827097 | 0.102232404 | 0.20397376 | 0.21324553 | 0.11250753 | 0.21525614 | 0.13500223 | 0.19044247 | 0.12454531 | 0.06680912 | 0.19270478 | 0.26318517 | 0.21065743 | 0.12102757 | 0.27029777 | 0.2220647 | 0.22582535 | 0.008082758 | 0.12526362 | 0.13911659 | 0.15426102 | 0.10477927 | 0.13112706 | 0.006145498 | 0.17138627 |
| 18 | recall | 0.5093642 | 0.14043704 | 0.78571427 | 0.41666666 | 0.61904764 | 0.41666666 | 0.3125 | 0.625 | 0.4117647 | 0.5 | 0.5625 | 0.33333334 | 0.53333336 | 0.44444445 | 0.44444445 | 0.5714286 | 0.375 | 0.5625 | 0.6666667 | 0.5 | 0.64285713 | 0.6363636 | 0.625 | 0.46666667 | 0.14285715 | 0.53333336 | 0.6 | 0.42857143 | 0.4117647 | 0.4 | 0.8125 | 0.5 |
| 19 | residual_deviance | 95.68137 | 16.045015 | 69.575775 | 86.837425 | 105.64646 | 86.2394 | 116.59676 | 87.81372 | 106.44169 | 83.454 | 90.441925 | 100.67192 | 87.238686 | 111.06961 | 103.33638 | 124.74725 | 107.72204 | 92.49677 | 80.82213 | 73.31486 | 89.512535 | 63.668667 | 89.90398 | 88.05111 | 107.408264 | 96.70126 | 68.27355 | 123.22077 | 106.4828 | 94.219925 | 113.640015 | 114.89139 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:11:48 | 0.000 sec | 2 | .89E1 | 14.0 | 0.452088 | 0.452238 | 0.452319 | 0.010752 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:11:48 | 0.003 sec | 4 | .55E1 | 14.0 | 0.450633 | 0.450924 | 0.450928 | 0.010714 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:11:48 | 0.005 sec | 6 | .34E1 | 15.0 | 0.448342 | 0.44886 | 0.448738 | 0.010655 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:11:48 | 0.008 sec | 8 | .21E1 | 15.0 | 0.444775 | 0.445654 | 0.445323 | 0.010568 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:11:48 | 0.011 sec | 10 | .13E1 | 15.0 | 0.439382 | 0.440827 | 0.440148 | 0.010445 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:11:48 | 0.013 sec | 12 | .82E0 | 15.0 | 0.431571 | 0.43389 | 0.432623 | 0.01029 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:11:48 | 0.015 sec | 14 | .51E0 | 15.0 | 0.42108 | 0.424695 | 0.422455 | 0.01013 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:11:48 | 0.018 sec | 16 | .32E0 | 15.0 | 0.40867 | 0.414064 | 0.410313 | 0.010034 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:11:48 | 0.020 sec | 18 | .2E0 | 15.0 | 0.396339 | 0.403917 | 0.398122 | 0.010075 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:11:48 | 0.023 sec | 20 | .12E0 | 15.0 | 0.386218 | 0.396125 | 0.388065 | 0.010265 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:11:48 | 0.025 sec | 22 | .76E-1 | 15.0 | 0.379121 | 0.391205 | 0.381055 | 0.010541 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:11:48 | 0.028 sec | 24 | .47E-1 | 15.0 | 0.37463 | 0.388523 | 0.376728 | 0.010828 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:11:48 | 0.031 sec | 26 | .29E-1 | 15.0 | 0.371947 | 0.387191 | 0.374274 | 0.011081 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:11:48 | 0.033 sec | 28 | .18E-1 | 15.0 | 0.370383 | 0.386524 | 0.37251 | 0.011211 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:11:48 | 0.036 sec | 30 | .11E-1 | 15.0 | 0.369485 | 0.386134 | 0.371689 | 0.011335 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:11:48 | 0.265 sec | 31 | None | NaN | 31.0 | 0.218376 | 0.184489 | 0.155885 | 0.777525 | 0.29919 | 9.598126 | 0.075392 | 0.223115 | 0.192926 | 0.118645 | 0.744823 | 0.251673 | 6.65641 | 0.085259 | ||||||
| 16 | 2021-07-15 20:11:48 | 0.039 sec | 32 | .7E-2 | 15.0 | 0.368978 | 0.385852 | 0.371218 | 0.011412 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:11:48 | 0.041 sec | 34 | .44E-2 | 15.0 | 0.368703 | 0.385628 | 0.372234 | 0.011736 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:11:48 | 0.043 sec | 35 | .27E-2 | 15.0 | 0.368561 | 0.385456 | 0.372097 | 0.011769 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:11:48 | 0.044 sec | 36 | .17E-2 | 15.0 | 0.36849 | 0.385322 | 0.377618 | 0.012857 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.579086 | 1.000000 | 0.278830 |
| 1 | Average_Transaction_Frequency | 0.239369 | 0.413357 | 0.115256 |
| 2 | Merchant_ID | 0.221707 | 0.382857 | 0.106752 |
| 3 | Minimum_Transaction_Amount | 0.178336 | 0.307961 | 0.085869 |
| 4 | Channel_ID | 0.163260 | 0.281927 | 0.078610 |
| 5 | Card_Type.1 | 0.152321 | 0.263037 | 0.073343 |
| 6 | Card_Type.0 | 0.150138 | 0.259268 | 0.072292 |
| 7 | Transaction_Amount | 0.141580 | 0.244490 | 0.068171 |
| 8 | Day | 0.068137 | 0.117663 | 0.032808 |
| 9 | Transaction_Date | 0.065619 | 0.113315 | 0.031596 |
| 10 | Month | 0.056053 | 0.096796 | 0.026990 |
| 11 | Average_Transaction_Amount | 0.022637 | 0.039091 | 0.010900 |
| 12 | Maximum_Transaction_Amount | 0.022012 | 0.038012 | 0.010599 |
| 13 | City_ID | 0.016587 | 0.028643 | 0.007986 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201151 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01096 ) | nlambda = 30, lambda.max = 8.6375, lambda.min = 0.01096, lambda.1s... | 14 | 14 | 30 | automl_training_py_131_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04823667005641544 RMSE: 0.21962848188797243 LogLoss: 0.18668272561176852 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938366 Residual deviance: 2907.0234032264602 AIC: 2937.0234032264602 AUC: 0.773431277052485 AUCPR: 0.2879936070472119 Gini: 0.5468625541049701 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.251742278200434:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7004.0 | 314.0 | 0.0429 | (314.0/7318.0) |
| 1 | 1 | 268.0 | 200.0 | 0.5726 | (268.0/468.0) |
| 2 | Total | 7272.0 | 514.0 | 0.0747 | (582.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.251742 | 0.407332 | 152.0 |
| 1 | max f2 | 0.062647 | 0.442164 | 226.0 |
| 2 | max f0point5 | 0.329606 | 0.411426 | 110.0 |
| 3 | max accuracy | 0.442953 | 0.940663 | 33.0 |
| 4 | max precision | 0.567586 | 0.714286 | 6.0 |
| 5 | max recall | 0.020563 | 1.000000 | 379.0 |
| 6 | max specificity | 0.813347 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.251742 | 0.367995 | 152.0 |
| 8 | max min_per_class_accuracy | 0.042153 | 0.696092 | 285.0 |
| 9 | max mean_per_class_accuracy | 0.062153 | 0.714509 | 227.0 |
| 10 | max tns | 0.813347 | 7317.000000 | 0.0 |
| 11 | max fns | 0.813347 | 468.000000 | 0.0 |
| 12 | max fps | 0.002163 | 7318.000000 | 399.0 |
| 13 | max tps | 0.020563 | 468.000000 | 379.0 |
| 14 | max tnr | 0.813347 | 0.999863 | 0.0 |
| 15 | max fnr | 0.813347 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002163 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020563 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.422245 | 8.744959 | 8.744959 | 0.525641 | 0.483894 | 0.525641 | 0.483894 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.385219 | 7.465209 | 8.105084 | 0.448718 | 0.403112 | 0.487179 | 0.443503 | 0.074786 | 0.162393 | 646.520929 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.360480 | 6.185459 | 7.465209 | 0.371795 | 0.372705 | 0.448718 | 0.419904 | 0.061966 | 0.224359 | 518.545913 | 646.520929 | 0.206731 |
| 3 | 4 | 0.040072 | 0.340920 | 6.185459 | 7.145272 | 0.371795 | 0.349399 | 0.429487 | 0.402278 | 0.061966 | 0.286325 | 518.545913 | 614.527175 | 0.262001 |
| 4 | 5 | 0.050090 | 0.321067 | 6.398751 | 6.995968 | 0.384615 | 0.331441 | 0.420513 | 0.388110 | 0.064103 | 0.350427 | 539.875082 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.063943 | 2.993760 | 4.997433 | 0.179949 | 0.162135 | 0.300385 | 0.275268 | 0.149573 | 0.500000 | 199.376002 | 399.743261 | 0.425526 |
| 6 | 7 | 0.150013 | 0.052065 | 0.940896 | 3.646411 | 0.056555 | 0.056681 | 0.219178 | 0.202468 | 0.047009 | 0.547009 | -5.910399 | 264.641143 | 0.422384 |
| 7 | 8 | 0.200103 | 0.047411 | 0.853167 | 2.947204 | 0.051282 | 0.049446 | 0.177150 | 0.164163 | 0.042735 | 0.589744 | -14.683322 | 194.720384 | 0.414559 |
| 8 | 9 | 0.300026 | 0.043026 | 0.940896 | 2.279007 | 0.056555 | 0.045023 | 0.136986 | 0.124484 | 0.094017 | 0.683761 | -5.910399 | 127.900714 | 0.408276 |
| 9 | 10 | 0.400077 | 0.039777 | 0.597983 | 1.858616 | 0.035944 | 0.041348 | 0.111717 | 0.103693 | 0.059829 | 0.743590 | -40.201661 | 85.861629 | 0.365481 |
| 10 | 11 | 0.500000 | 0.036990 | 0.406296 | 1.568376 | 0.024422 | 0.038372 | 0.094272 | 0.090639 | 0.040598 | 0.784188 | -59.370400 | 56.837607 | 0.302362 |
| 11 | 12 | 0.600051 | 0.034433 | 0.726123 | 1.427940 | 0.043646 | 0.035694 | 0.085830 | 0.081478 | 0.072650 | 0.856838 | -27.387731 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.031929 | 0.555984 | 1.303467 | 0.033419 | 0.033196 | 0.078349 | 0.074585 | 0.055556 | 0.912393 | -44.401600 | 30.346664 | 0.226003 |
| 13 | 14 | 0.800026 | 0.029082 | 0.384418 | 1.188530 | 0.023107 | 0.030572 | 0.071440 | 0.069081 | 0.038462 | 0.950855 | -61.558211 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.025261 | 0.320760 | 1.092180 | 0.019280 | 0.027440 | 0.065649 | 0.064457 | 0.032051 | 0.982906 | -67.924000 | 9.218010 | 0.088263 |
| 15 | 16 | 1.000000 | 0.001692 | 0.170852 | 1.000000 | 0.010270 | 0.020984 | 0.060108 | 0.060108 | 0.017094 | 1.000000 | -82.914760 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.048047477224484776 RMSE: 0.21919734766754084 LogLoss: 0.18565530328379584 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311525 Residual deviance: 722.9417509871014 AIC: 752.9417509871014 AUC: 0.761986829199944 AUCPR: 0.28237420053427037 Gini: 0.523973658399888 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.08892369711113483:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1739.0 | 91.0 | 0.0497 | (91.0/1830.0) |
| 1 | 1 | 59.0 | 58.0 | 0.5043 | (59.0/117.0) |
| 2 | Total | 1798.0 | 149.0 | 0.077 | (150.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.088924 | 0.436090 | 125.0 |
| 1 | max f2 | 0.088924 | 0.470016 | 125.0 |
| 2 | max f0point5 | 0.303835 | 0.431193 | 88.0 |
| 3 | max accuracy | 0.504930 | 0.940935 | 3.0 |
| 4 | max precision | 0.504930 | 0.750000 | 3.0 |
| 5 | max recall | 0.021862 | 1.000000 | 364.0 |
| 6 | max specificity | 0.795808 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.088924 | 0.398717 | 125.0 |
| 8 | max min_per_class_accuracy | 0.041164 | 0.683761 | 233.0 |
| 9 | max mean_per_class_accuracy | 0.075607 | 0.723721 | 133.0 |
| 10 | max tns | 0.795808 | 1829.000000 | 0.0 |
| 11 | max fns | 0.795808 | 117.000000 | 0.0 |
| 12 | max fps | 0.001996 | 1830.000000 | 399.0 |
| 13 | max tps | 0.021862 | 117.000000 | 364.0 |
| 14 | max tnr | 0.795808 | 0.999454 | 0.0 |
| 15 | max fnr | 0.795808 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001996 | 1.000000 | 399.0 |
| 17 | max tpr | 0.021862 | 1.000000 | 364.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.85 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.405461 | 7.488462 | 7.488462 | 0.450000 | 0.479730 | 0.450000 | 0.479730 | 0.076923 | 0.076923 | 648.846154 | 648.846154 | 0.070912 |
| 1 | 2 | 0.020031 | 0.374834 | 7.882591 | 7.680473 | 0.473684 | 0.388245 | 0.461538 | 0.435160 | 0.076923 | 0.153846 | 688.259109 | 668.047337 | 0.142371 |
| 2 | 3 | 0.030303 | 0.352666 | 7.488462 | 7.615385 | 0.450000 | 0.363532 | 0.457627 | 0.410880 | 0.076923 | 0.230769 | 648.846154 | 661.538462 | 0.213283 |
| 3 | 4 | 0.040062 | 0.334982 | 7.006748 | 7.467127 | 0.421053 | 0.343118 | 0.448718 | 0.394374 | 0.068376 | 0.299145 | 600.674764 | 646.712689 | 0.275648 |
| 4 | 5 | 0.050334 | 0.315462 | 6.656410 | 7.301675 | 0.400000 | 0.325656 | 0.438776 | 0.380350 | 0.068376 | 0.367521 | 565.641026 | 630.167452 | 0.337467 |
| 5 | 6 | 0.100154 | 0.060680 | 2.916468 | 5.120316 | 0.175258 | 0.146198 | 0.307692 | 0.263874 | 0.145299 | 0.512821 | 191.646841 | 412.031558 | 0.439050 |
| 6 | 7 | 0.149974 | 0.051220 | 0.514671 | 3.590358 | 0.030928 | 0.055022 | 0.215753 | 0.194495 | 0.025641 | 0.538462 | -48.532910 | 259.035827 | 0.413325 |
| 7 | 8 | 0.200308 | 0.047012 | 0.679226 | 2.858843 | 0.040816 | 0.048938 | 0.171795 | 0.157919 | 0.034188 | 0.572650 | -32.077446 | 185.884287 | 0.396147 |
| 8 | 9 | 0.299949 | 0.042345 | 0.943563 | 2.222603 | 0.056701 | 0.044477 | 0.133562 | 0.120235 | 0.094017 | 0.666667 | -5.643669 | 122.260274 | 0.390164 |
| 9 | 10 | 0.400103 | 0.038811 | 0.341354 | 1.751687 | 0.020513 | 0.040601 | 0.105263 | 0.100301 | 0.034188 | 0.700855 | -65.864563 | 75.168691 | 0.319980 |
| 10 | 11 | 0.500257 | 0.036157 | 0.682709 | 1.537672 | 0.041026 | 0.037398 | 0.092402 | 0.087707 | 0.068376 | 0.769231 | -31.729126 | 53.767177 | 0.286171 |
| 11 | 12 | 0.599897 | 0.034067 | 0.600449 | 1.382003 | 0.036082 | 0.035099 | 0.083048 | 0.078969 | 0.059829 | 0.829060 | -39.955062 | 38.200299 | 0.243814 |
| 12 | 13 | 0.700051 | 0.031684 | 0.853386 | 1.306375 | 0.051282 | 0.032797 | 0.078503 | 0.072363 | 0.085470 | 0.914530 | -14.661407 | 30.637545 | 0.228191 |
| 13 | 14 | 0.799692 | 0.029025 | 0.171557 | 1.164979 | 0.010309 | 0.030413 | 0.070006 | 0.067136 | 0.017094 | 0.931624 | -82.844303 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.025700 | 0.512032 | 1.092305 | 0.030769 | 0.027542 | 0.065639 | 0.062729 | 0.051282 | 0.982906 | -48.796844 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.001996 | 0.170677 | 1.000000 | 0.010256 | 0.020857 | 0.060092 | 0.058536 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04850061081473769 RMSE: 0.22022854223451074 LogLoss: 0.18803695755567185 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.407751090541 Residual deviance: 2928.1115030569213 AIC: 2958.1115030569213 AUC: 0.7598574408495151 AUCPR: 0.2759121285359448 Gini: 0.5197148816990302 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.24348651565127977:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7001.0 | 317.0 | 0.0433 | (317.0/7318.0) |
| 1 | 1 | 268.0 | 200.0 | 0.5726 | (268.0/468.0) |
| 2 | Total | 7269.0 | 517.0 | 0.0751 | (585.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.243487 | 0.406091 | 152.0 |
| 1 | max f2 | 0.061423 | 0.433900 | 226.0 |
| 2 | max f0point5 | 0.325263 | 0.403061 | 111.0 |
| 3 | max accuracy | 0.559065 | 0.940406 | 8.0 |
| 4 | max precision | 0.559065 | 0.700000 | 8.0 |
| 5 | max recall | 0.019905 | 1.000000 | 379.0 |
| 6 | max specificity | 0.840535 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.243487 | 0.366610 | 152.0 |
| 8 | max min_per_class_accuracy | 0.042015 | 0.688034 | 282.0 |
| 9 | max mean_per_class_accuracy | 0.061423 | 0.710005 | 226.0 |
| 10 | max tns | 0.840535 | 7317.000000 | 0.0 |
| 11 | max fns | 0.840535 | 468.000000 | 0.0 |
| 12 | max fps | 0.001459 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019905 | 468.000000 | 379.0 |
| 14 | max tnr | 0.840535 | 0.999863 | 0.0 |
| 15 | max fnr | 0.840535 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001459 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019905 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.422254 | 8.105084 | 8.105084 | 0.487179 | 0.484785 | 0.487179 | 0.484785 | 0.081197 | 0.081197 | 710.508437 | 710.508437 | 0.075731 |
| 1 | 2 | 0.020036 | 0.385595 | 7.891793 | 7.998439 | 0.474359 | 0.401685 | 0.480769 | 0.443235 | 0.079060 | 0.160256 | 689.179268 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.359823 | 6.612043 | 7.536307 | 0.397436 | 0.371749 | 0.452991 | 0.419406 | 0.066239 | 0.226496 | 561.204252 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.339520 | 5.545584 | 7.038626 | 0.333333 | 0.349653 | 0.423077 | 0.401968 | 0.055556 | 0.282051 | 454.558405 | 603.862590 | 0.257454 |
| 4 | 5 | 0.050090 | 0.320100 | 6.398751 | 6.910651 | 0.384615 | 0.329997 | 0.415385 | 0.387574 | 0.064103 | 0.346154 | 539.875082 | 591.065089 | 0.314998 |
| 5 | 6 | 0.100051 | 0.064092 | 2.822688 | 4.869293 | 0.169666 | 0.162180 | 0.292683 | 0.275021 | 0.141026 | 0.487179 | 182.268802 | 386.929331 | 0.411886 |
| 6 | 7 | 0.150013 | 0.051984 | 0.983664 | 3.575192 | 0.059126 | 0.056722 | 0.214897 | 0.202317 | 0.049145 | 0.536325 | -1.633599 | 257.519245 | 0.411017 |
| 7 | 8 | 0.200103 | 0.047487 | 0.981142 | 2.925847 | 0.058974 | 0.049495 | 0.175866 | 0.164062 | 0.049145 | 0.585470 | -1.885821 | 192.584729 | 0.410012 |
| 8 | 9 | 0.300026 | 0.042985 | 0.812592 | 2.222032 | 0.048843 | 0.045068 | 0.133562 | 0.124432 | 0.081197 | 0.666667 | -18.740799 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.039822 | 0.640696 | 1.826571 | 0.038511 | 0.041344 | 0.109791 | 0.103653 | 0.064103 | 0.730769 | -35.930351 | 82.657118 | 0.351841 |
| 10 | 11 | 0.500000 | 0.036937 | 0.320760 | 1.525641 | 0.019280 | 0.038409 | 0.091703 | 0.090614 | 0.032051 | 0.762821 | -67.924000 | 52.564103 | 0.279628 |
| 11 | 12 | 0.600051 | 0.034469 | 0.640696 | 1.378087 | 0.038511 | 0.035720 | 0.082834 | 0.081462 | 0.064103 | 0.826923 | -35.930351 | 37.808713 | 0.241381 |
| 12 | 13 | 0.699974 | 0.032066 | 0.684288 | 1.279046 | 0.041131 | 0.033258 | 0.076881 | 0.074580 | 0.068376 | 0.895299 | -31.571199 | 27.904571 | 0.207816 |
| 13 | 14 | 0.800026 | 0.029266 | 0.491201 | 1.180518 | 0.029525 | 0.030680 | 0.070958 | 0.069090 | 0.049145 | 0.944444 | -50.879936 | 18.051765 | 0.153655 |
| 14 | 15 | 0.899949 | 0.025372 | 0.299376 | 1.082683 | 0.017995 | 0.027544 | 0.065078 | 0.064477 | 0.029915 | 0.974359 | -70.062400 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.001224 | 0.256279 | 1.000000 | 0.015404 | 0.021045 | 0.060108 | 0.060132 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9308875 | 0.031215278 | 0.88846153 | 0.96153843 | 0.9230769 | 0.9461538 | 0.97692305 | 0.9153846 | 0.96153843 | 0.9269231 | 0.97307694 | 0.91923076 | 0.95384616 | 0.9153846 | 0.90384614 | 0.95384616 | 0.9692308 | 0.9153846 | 0.9498069 | 0.9459459 | 0.93050194 | 0.93822396 | 0.93822396 | 0.8146718 | 0.93050194 | 0.9459459 | 0.9266409 | 0.9034749 | 0.9227799 | 0.9459459 | 0.9034749 | 0.9266409 |
| 1 | auc | 0.7671368 | 0.064129576 | 0.7868101 | 0.78911567 | 0.79939514 | 0.83736557 | 0.9087258 | 0.8265881 | 0.7050763 | 0.8036689 | 0.8052235 | 0.7754167 | 0.7916537 | 0.7849936 | 0.6670082 | 0.7601116 | 0.84912634 | 0.6804648 | 0.7890351 | 0.78104955 | 0.7313253 | 0.6293333 | 0.7093294 | 0.722489 | 0.6872807 | 0.7426686 | 0.77521867 | 0.69255316 | 0.7502572 | 0.8623482 | 0.8485477 | 0.72192514 |
| 2 | err | 0.069112465 | 0.031215278 | 0.11153846 | 0.03846154 | 0.07692308 | 0.053846154 | 0.023076924 | 0.08461539 | 0.03846154 | 0.073076926 | 0.026923077 | 0.08076923 | 0.046153847 | 0.08461539 | 0.09615385 | 0.046153847 | 0.03076923 | 0.08461539 | 0.05019305 | 0.054054055 | 0.06949807 | 0.06177606 | 0.06177606 | 0.18532819 | 0.06949807 | 0.054054055 | 0.07335907 | 0.096525095 | 0.077220075 | 0.054054055 | 0.096525095 | 0.07335907 |
| 3 | err_count | 17.933332 | 8.089726 | 29.0 | 10.0 | 20.0 | 14.0 | 6.0 | 22.0 | 10.0 | 19.0 | 7.0 | 21.0 | 12.0 | 22.0 | 25.0 | 12.0 | 8.0 | 22.0 | 13.0 | 14.0 | 18.0 | 16.0 | 16.0 | 48.0 | 18.0 | 14.0 | 19.0 | 25.0 | 20.0 | 14.0 | 25.0 | 19.0 |
| 4 | f0point5 | 0.46647963 | 0.14185402 | 0.40268457 | 0.6862745 | 0.29761904 | 0.44117647 | 0.72727275 | 0.4017857 | 0.6097561 | 0.48192772 | 0.6 | 0.45454547 | 0.5555556 | 0.5217391 | 0.27173913 | 0.7303371 | 0.6944444 | 0.3529412 | 0.6666667 | 0.51282054 | 0.22727273 | 0.24590164 | 0.44871795 | 0.27896994 | 0.45454547 | 0.4225352 | 0.4245283 | 0.45 | 0.375 | 0.47619048 | 0.3968254 | 0.3846154 |
| 5 | f1 | 0.4587033 | 0.11546789 | 0.4528302 | 0.5833333 | 0.33333334 | 0.46153846 | 0.72727275 | 0.45 | 0.5 | 0.45714286 | 0.46153846 | 0.43243244 | 0.6 | 0.5217391 | 0.2857143 | 0.68421054 | 0.5555556 | 0.3529412 | 0.6060606 | 0.53333336 | 0.25 | 0.27272728 | 0.46666667 | 0.35135135 | 0.35714287 | 0.46153846 | 0.4864865 | 0.41860464 | 0.375 | 0.53333336 | 0.44444445 | 0.3448276 |
| 6 | f2 | 0.46413183 | 0.11503928 | 0.51724136 | 0.5072464 | 0.37878788 | 0.48387095 | 0.72727275 | 0.5113636 | 0.42372882 | 0.4347826 | 0.375 | 0.41237113 | 0.65217394 | 0.5217391 | 0.30120483 | 0.64356434 | 0.46296296 | 0.3529412 | 0.5555556 | 0.5555556 | 0.2777778 | 0.30612245 | 0.4861111 | 0.47445256 | 0.29411766 | 0.5084746 | 0.56962025 | 0.39130434 | 0.375 | 0.6060606 | 0.5050505 | 0.3125 |
| 7 | lift_top_group | 8.655453 | 5.161792 | 8.253968 | 17.333334 | 7.2222223 | 7.2222223 | 15.757576 | 5.4166665 | 13.333333 | 9.122807 | 19.25926 | 8.666667 | 13.333333 | 7.536232 | 0.0 | 12.380953 | 14.444445 | 5.098039 | 13.631579 | 12.333333 | 0.0 | 0.0 | 0.0 | 4.111111 | 9.087719 | 7.848485 | 12.333333 | 3.5972223 | 5.3958335 | 7.1944447 | 9.592592 | 10.156863 |
| 8 | logloss | 0.18641977 | 0.043424655 | 0.24064456 | 0.15456016 | 0.16561452 | 0.14941609 | 0.10523636 | 0.18000935 | 0.16949518 | 0.21290039 | 0.12968749 | 0.23132138 | 0.14102696 | 0.23450491 | 0.21825534 | 0.19931853 | 0.14720972 | 0.22004405 | 0.19242805 | 0.16002396 | 0.15663557 | 0.14838272 | 0.1767577 | 0.27106014 | 0.23598683 | 0.14550698 | 0.16298485 | 0.28430513 | 0.19972184 | 0.1417392 | 0.20247543 | 0.21533969 |
| 9 | max_per_class_error | 0.5244442 | 0.13055377 | 0.42857143 | 0.53333336 | 0.5833333 | 0.5 | 0.27272728 | 0.4375 | 0.61538464 | 0.57894737 | 0.6666667 | 0.6 | 0.30769232 | 0.47826087 | 0.6875 | 0.3809524 | 0.5833333 | 0.64705884 | 0.47368422 | 0.42857143 | 0.7 | 0.6666667 | 0.5 | 0.3809524 | 0.7368421 | 0.45454547 | 0.35714287 | 0.625 | 0.625 | 0.33333334 | 0.44444445 | 0.7058824 |
| 10 | mcc | 0.43318447 | 0.12531078 | 0.40453902 | 0.58480775 | 0.30107096 | 0.43480167 | 0.71522456 | 0.41596073 | 0.50698704 | 0.4200517 | 0.48917568 | 0.39075184 | 0.58181274 | 0.4753256 | 0.23556952 | 0.6638989 | 0.5766196 | 0.3076737 | 0.5876336 | 0.5060481 | 0.21796799 | 0.24604438 | 0.43512467 | 0.30512953 | 0.35090747 | 0.4395816 | 0.46559998 | 0.3697254 | 0.33384773 | 0.5175998 | 0.40362224 | 0.31243056 |
| 11 | mean_per_class_accuracy | 0.71765965 | 0.06335363 | 0.7438733 | 0.7292517 | 0.68212366 | 0.733871 | 0.85761225 | 0.7505123 | 0.6882591 | 0.6939288 | 0.66467464 | 0.68125 | 0.8299595 | 0.7376628 | 0.62756145 | 0.8011556 | 0.7063172 | 0.65383685 | 0.7548246 | 0.7693878 | 0.6279116 | 0.64666665 | 0.73163265 | 0.7254902 | 0.6232456 | 0.7545821 | 0.79285717 | 0.6662234 | 0.6669239 | 0.81309044 | 0.74250805 | 0.632596 |
| 12 | mean_per_class_error | 0.28234032 | 0.06335363 | 0.25612673 | 0.2707483 | 0.31787634 | 0.26612905 | 0.14238773 | 0.2494877 | 0.3117409 | 0.3060712 | 0.33532536 | 0.31875 | 0.17004049 | 0.26233718 | 0.37243852 | 0.19884439 | 0.29368278 | 0.34616315 | 0.24517544 | 0.23061225 | 0.37208834 | 0.35333332 | 0.26836735 | 0.27450982 | 0.37675437 | 0.2454179 | 0.20714286 | 0.3337766 | 0.33307612 | 0.18690959 | 0.25749195 | 0.36740398 |
| 13 | mse | 0.048019834 | 0.013066919 | 0.064429164 | 0.03794699 | 0.040989734 | 0.037924293 | 0.02467804 | 0.047978368 | 0.04128984 | 0.05673737 | 0.030061362 | 0.062119078 | 0.03462969 | 0.06421524 | 0.055968758 | 0.05257439 | 0.036574233 | 0.057234302 | 0.05034259 | 0.04030629 | 0.0387647 | 0.03368127 | 0.04500596 | 0.072213985 | 0.06180048 | 0.034815498 | 0.041341543 | 0.07797975 | 0.051939677 | 0.036763333 | 0.055260167 | 0.05502889 |
| 14 | null_deviance | 118.04693 | 23.11029 | 147.85797 | 114.7255 | 98.28862 | 98.28862 | 92.82863 | 120.223526 | 103.75808 | 136.7752 | 81.936905 | 142.31174 | 103.75808 | 158.97968 | 120.223526 | 147.85797 | 98.28862 | 125.73113 | 136.65318 | 109.11213 | 87.25086 | 81.80913 | 109.11213 | 147.73709 | 136.65318 | 92.701996 | 109.11213 | 164.43605 | 120.09978 | 98.16257 | 131.12575 | 125.607956 |
| 15 | pr_auc | 0.324166 | 0.1324039 | 0.3836906 | 0.56136936 | 0.22763278 | 0.26025802 | 0.5797453 | 0.30295363 | 0.27921215 | 0.33521745 | 0.3462151 | 0.3418799 | 0.4456629 | 0.43137068 | 0.13588724 | 0.55796874 | 0.48607692 | 0.18324837 | 0.5622041 | 0.35118216 | 0.103380375 | 0.08858764 | 0.224178 | 0.21748461 | 0.30055645 | 0.31455514 | 0.34558007 | 0.24668717 | 0.21951066 | 0.31458074 | 0.30305144 | 0.27505246 |
| 16 | precision | 0.48102373 | 0.17715605 | 0.375 | 0.7777778 | 0.2777778 | 0.42857143 | 0.72727275 | 0.375 | 0.71428573 | 0.5 | 0.75 | 0.47058824 | 0.5294118 | 0.5217391 | 0.2631579 | 0.7647059 | 0.8333333 | 0.3529412 | 0.71428573 | 0.5 | 0.21428572 | 0.23076923 | 0.4375 | 0.24528302 | 0.5555556 | 0.4 | 0.39130434 | 0.47368422 | 0.375 | 0.44444445 | 0.37037036 | 0.41666666 |
| 17 | r2 | 0.14475171 | 0.09457928 | 0.13221532 | 0.30198187 | 0.06891602 | 0.13854767 | 0.39093262 | 0.16922705 | 0.1307402 | 0.16238342 | 0.10042139 | 0.12515631 | 0.2709539 | 0.20364147 | 0.03086882 | 0.29188508 | 0.16921434 | 0.06341348 | 0.25942296 | 0.21172418 | -0.04432728 | -0.004165844 | 0.11981204 | 0.030775031 | 0.090868875 | 0.14389351 | 0.19147749 | 0.07252487 | 0.1038669 | 0.16797537 | 0.1454801 | 0.102724165 |
| 18 | recall | 0.47555578 | 0.13055377 | 0.5714286 | 0.46666667 | 0.41666666 | 0.5 | 0.72727275 | 0.5625 | 0.3846154 | 0.42105263 | 0.33333334 | 0.4 | 0.6923077 | 0.5217391 | 0.3125 | 0.61904764 | 0.41666666 | 0.3529412 | 0.5263158 | 0.5714286 | 0.3 | 0.33333334 | 0.5 | 0.61904764 | 0.2631579 | 0.54545456 | 0.64285713 | 0.375 | 0.375 | 0.6666667 | 0.5555556 | 0.29411766 |
| 19 | residual_deviance | 96.75872 | 22.512697 | 125.13517 | 80.37129 | 86.11955 | 77.696365 | 54.722908 | 93.60486 | 88.1375 | 110.7082 | 67.43749 | 120.28712 | 73.334015 | 121.94255 | 113.492775 | 103.64563 | 76.54906 | 114.422905 | 99.677734 | 82.89241 | 81.13722 | 76.86225 | 91.560486 | 140.40915 | 122.24117 | 75.37261 | 84.426155 | 147.27005 | 103.45591 | 73.420906 | 104.88227 | 111.54596 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:11:59 | 0.000 sec | 2 | .86E1 | 15 | 0.452139 | 0.452086 | 0.452559 | 0.016186 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:11:59 | 0.002 sec | 4 | .54E1 | 15 | 0.450715 | 0.450681 | 0.451197 | 0.016143 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:11:59 | 0.005 sec | 6 | .33E1 | 15 | 0.448477 | 0.448472 | 0.449055 | 0.016078 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:11:59 | 0.008 sec | 8 | .21E1 | 15 | 0.444997 | 0.445035 | 0.445718 | 0.015978 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:11:59 | 0.012 sec | 10 | .13E1 | 15 | 0.439747 | 0.439845 | 0.440674 | 0.015834 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:11:59 | 0.014 sec | 12 | .8E0 | 15 | 0.432174 | 0.432348 | 0.433369 | 0.015642 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:11:59 | 0.017 sec | 14 | .5E0 | 15 | 0.422064 | 0.422311 | 0.423553 | 0.015417 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:11:59 | 0.020 sec | 16 | .31E0 | 15 | 0.410197 | 0.410467 | 0.411919 | 0.015218 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:11:59 | 0.022 sec | 18 | .19E0 | 15 | 0.398515 | 0.398697 | 0.400343 | 0.015127 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:11:59 | 0.025 sec | 20 | .12E0 | 15 | 0.389017 | 0.388960 | 0.390879 | 0.015183 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:11:59 | 0.027 sec | 22 | .74E-1 | 15 | 0.382403 | 0.381982 | 0.384329 | 0.015350 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:11:59 | 0.030 sec | 24 | .46E-1 | 15 | 0.378237 | 0.377383 | 0.380301 | 0.015558 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:11:59 | 0.032 sec | 26 | .28E-1 | 15 | 0.375735 | 0.374442 | 0.378002 | 0.015754 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:11:59 | 0.035 sec | 28 | .18E-1 | 15 | 0.374249 | 0.372548 | 0.376747 | 0.015916 | 0.0 | 28.0 | 0.219628 | 0.186683 | 0.146177 | 0.773431 | 0.287994 | 8.744959 | 0.07475 | 0.219197 | 0.185655 | 0.149321 | 0.761987 | 0.282374 | 7.488462 | 0.077042 | |
| 14 | 2021-07-15 20:11:59 | 0.037 sec | 30 | .11E-1 | 15 | 0.373365 | 0.371311 | 0.376096 | 0.016038 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:11:59 | 0.040 sec | 32 | .68E-2 | 15 | 0.372845 | 0.370503 | 0.376828 | 0.016396 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:11:59 | 0.042 sec | 34 | .42E-2 | 15 | 0.372548 | 0.369985 | 0.377127 | 0.016376 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:11:59 | 0.045 sec | 36 | .26E-2 | 15 | 0.372383 | 0.369661 | 0.383233 | 0.017870 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.550464 | 1.000000 | 0.277891 |
| 1 | Average_Transaction_Frequency | 0.218080 | 0.396175 | 0.110093 |
| 2 | Merchant_ID | 0.200936 | 0.365030 | 0.101439 |
| 3 | Channel_ID | 0.168577 | 0.306244 | 0.085102 |
| 4 | Minimum_Transaction_Amount | 0.164149 | 0.298202 | 0.082867 |
| 5 | Card_Type.1 | 0.131562 | 0.239002 | 0.066416 |
| 6 | Card_Type.0 | 0.130267 | 0.236650 | 0.065763 |
| 7 | Transaction_Amount | 0.103948 | 0.188837 | 0.052476 |
| 8 | Transaction_Date | 0.081030 | 0.147204 | 0.040907 |
| 9 | Day | 0.068128 | 0.123766 | 0.034393 |
| 10 | Month | 0.050637 | 0.091989 | 0.025563 |
| 11 | Maximum_Transaction_Amount | 0.050066 | 0.090952 | 0.025275 |
| 12 | Average_Transaction_Amount | 0.034740 | 0.063110 | 0.017538 |
| 13 | City_ID | 0.028281 | 0.051377 | 0.014277 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201203 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.007084 ) | nlambda = 30, lambda.max = 8.9893, lambda.min = 0.007084, lambda.1... | 14 | 14 | 32 | automl_training_py_163_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04728100379734513 RMSE: 0.21744195500718147 LogLoss: 0.18232722730522372 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938307 Residual deviance: 2839.1995835969437 AIC: 2869.1995835969437 AUC: 0.7879397598241544 AUCPR: 0.3067125411061535 Gini: 0.5758795196483089 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2850920822899183:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7029.0 | 289.0 | 0.0395 | (289.0/7318.0) |
| 1 | 1 | 262.0 | 206.0 | 0.5598 | (262.0/468.0) |
| 2 | Total | 7291.0 | 495.0 | 0.0708 | (551.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.285092 | 0.427830 | 151.0 |
| 1 | max f2 | 0.061823 | 0.467825 | 240.0 |
| 2 | max f0point5 | 0.363284 | 0.434149 | 104.0 |
| 3 | max accuracy | 0.449624 | 0.940663 | 46.0 |
| 4 | max precision | 0.880720 | 1.000000 | 0.0 |
| 5 | max recall | 0.016296 | 1.000000 | 385.0 |
| 6 | max specificity | 0.880720 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.290148 | 0.390415 | 148.0 |
| 8 | max min_per_class_accuracy | 0.040013 | 0.705128 | 292.0 |
| 9 | max mean_per_class_accuracy | 0.058756 | 0.730801 | 244.0 |
| 10 | max tns | 0.880720 | 7318.000000 | 0.0 |
| 11 | max fns | 0.880720 | 467.000000 | 0.0 |
| 12 | max fps | 0.001173 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016296 | 468.000000 | 385.0 |
| 14 | max tnr | 0.880720 | 1.000000 | 0.0 |
| 15 | max fnr | 0.880720 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001173 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016296 | 1.000000 | 385.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.449588 | 8.744959 | 8.744959 | 0.525641 | 0.536233 | 0.525641 | 0.536233 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.409573 | 7.891793 | 8.318376 | 0.474359 | 0.425620 | 0.500000 | 0.480926 | 0.079060 | 0.166667 | 689.179268 | 731.837607 | 0.156008 |
| 2 | 3 | 0.030054 | 0.386462 | 6.825334 | 7.820695 | 0.410256 | 0.396793 | 0.470085 | 0.452882 | 0.068376 | 0.235043 | 582.533421 | 682.069545 | 0.218098 |
| 3 | 4 | 0.040072 | 0.362412 | 8.318376 | 7.945116 | 0.500000 | 0.374195 | 0.477564 | 0.433210 | 0.083333 | 0.318376 | 731.837607 | 694.511560 | 0.296102 |
| 4 | 5 | 0.050090 | 0.335214 | 4.905709 | 7.337234 | 0.294872 | 0.349546 | 0.441026 | 0.416478 | 0.049145 | 0.367521 | 390.570896 | 633.723428 | 0.337732 |
| 5 | 6 | 0.100051 | 0.064878 | 2.950992 | 5.146928 | 0.177378 | 0.172246 | 0.309371 | 0.294519 | 0.147436 | 0.514957 | 195.099202 | 414.692845 | 0.441440 |
| 6 | 7 | 0.150013 | 0.050383 | 1.111968 | 3.803093 | 0.066838 | 0.055775 | 0.228596 | 0.215006 | 0.055556 | 0.570513 | 11.196801 | 280.309317 | 0.447392 |
| 7 | 8 | 0.200103 | 0.045523 | 1.023800 | 3.107378 | 0.061538 | 0.047597 | 0.186778 | 0.173100 | 0.051282 | 0.621795 | 2.380013 | 210.737797 | 0.448660 |
| 8 | 9 | 0.300026 | 0.040505 | 0.684288 | 2.300373 | 0.041131 | 0.042721 | 0.138271 | 0.129677 | 0.068376 | 0.690171 | -31.571199 | 130.037283 | 0.415096 |
| 9 | 10 | 0.400077 | 0.037270 | 0.576627 | 1.869298 | 0.034660 | 0.038853 | 0.112360 | 0.106964 | 0.057692 | 0.747863 | -42.337316 | 86.929799 | 0.370028 |
| 10 | 11 | 0.500000 | 0.034893 | 0.748440 | 1.645299 | 0.044987 | 0.036057 | 0.098895 | 0.092794 | 0.074786 | 0.822650 | -25.155999 | 64.529915 | 0.343284 |
| 11 | 12 | 0.600051 | 0.032553 | 0.533914 | 1.459989 | 0.032092 | 0.033690 | 0.087757 | 0.082939 | 0.053419 | 0.876068 | -46.608626 | 45.998895 | 0.293669 |
| 12 | 13 | 0.699974 | 0.030108 | 0.299376 | 1.294309 | 0.017995 | 0.031321 | 0.077798 | 0.075570 | 0.029915 | 0.905983 | -70.062400 | 29.430879 | 0.219183 |
| 13 | 14 | 0.800026 | 0.027364 | 0.405774 | 1.183189 | 0.024390 | 0.028802 | 0.071119 | 0.069721 | 0.040598 | 0.946581 | -59.422556 | 18.318850 | 0.155928 |
| 14 | 15 | 0.899949 | 0.022624 | 0.320760 | 1.087431 | 0.019280 | 0.025330 | 0.065363 | 0.064793 | 0.032051 | 0.978632 | -67.924000 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000873 | 0.213565 | 1.000000 | 0.012837 | 0.017970 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.05131546546458558 RMSE: 0.22652917133249215 LogLoss: 0.20087706278503492 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311484 Residual deviance: 782.2152824849263 AIC: 812.2152824849263 AUC: 0.7173018541871 AUCPR: 0.22317424004052208 Gini: 0.43460370837420004 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.29582479261256667:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1759.0 | 71.0 | 0.0388 | (71.0/1830.0) |
| 1 | 1 | 78.0 | 39.0 | 0.6667 | (78.0/117.0) |
| 2 | Total | 1837.0 | 110.0 | 0.0765 | (149.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.295825 | 0.343612 | 92.0 |
| 1 | max f2 | 0.069421 | 0.358814 | 148.0 |
| 2 | max f0point5 | 0.349464 | 0.364706 | 63.0 |
| 3 | max accuracy | 0.605849 | 0.940935 | 1.0 |
| 4 | max precision | 0.790610 | 1.000000 | 0.0 |
| 5 | max recall | 0.020038 | 1.000000 | 365.0 |
| 6 | max specificity | 0.790610 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.295825 | 0.303183 | 92.0 |
| 8 | max min_per_class_accuracy | 0.038507 | 0.632479 | 249.0 |
| 9 | max mean_per_class_accuracy | 0.069421 | 0.661882 | 148.0 |
| 10 | max tns | 0.790610 | 1830.000000 | 0.0 |
| 11 | max fns | 0.790610 | 116.000000 | 0.0 |
| 12 | max fps | 0.000920 | 1830.000000 | 399.0 |
| 13 | max tps | 0.020038 | 117.000000 | 365.0 |
| 14 | max tnr | 0.790610 | 1.000000 | 0.0 |
| 15 | max fnr | 0.790610 | 0.991453 | 0.0 |
| 16 | max fpr | 0.000920 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020038 | 1.000000 | 365.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.86 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.450688 | 7.488462 | 7.488462 | 0.450000 | 0.512591 | 0.450000 | 0.512591 | 0.076923 | 0.076923 | 648.846154 | 648.846154 | 0.070912 |
| 1 | 2 | 0.020031 | 0.394607 | 5.255061 | 6.400394 | 0.315789 | 0.420423 | 0.384615 | 0.467689 | 0.051282 | 0.128205 | 425.506073 | 540.039448 | 0.115090 |
| 2 | 3 | 0.030303 | 0.369707 | 4.992308 | 5.923077 | 0.300000 | 0.380748 | 0.355932 | 0.438217 | 0.051282 | 0.179487 | 399.230769 | 492.307692 | 0.158722 |
| 3 | 4 | 0.040062 | 0.345246 | 8.758435 | 6.613741 | 0.526316 | 0.359480 | 0.397436 | 0.419038 | 0.085470 | 0.264957 | 775.843455 | 561.374096 | 0.239274 |
| 4 | 5 | 0.050334 | 0.322118 | 3.328205 | 5.943223 | 0.200000 | 0.335446 | 0.357143 | 0.401978 | 0.034188 | 0.299145 | 232.820513 | 494.322344 | 0.264719 |
| 5 | 6 | 0.100154 | 0.062845 | 1.887127 | 3.925575 | 0.113402 | 0.154609 | 0.235897 | 0.278928 | 0.094017 | 0.393162 | 88.712662 | 292.557528 | 0.311742 |
| 6 | 7 | 0.149974 | 0.049984 | 0.343114 | 2.735511 | 0.020619 | 0.054709 | 0.164384 | 0.204444 | 0.017094 | 0.410256 | -65.688607 | 173.551106 | 0.276923 |
| 7 | 8 | 0.200308 | 0.044870 | 0.849032 | 2.261473 | 0.051020 | 0.047497 | 0.135897 | 0.165006 | 0.042735 | 0.452991 | -15.096808 | 126.147272 | 0.268838 |
| 8 | 9 | 0.299949 | 0.040408 | 0.857785 | 1.795179 | 0.051546 | 0.042305 | 0.107877 | 0.124246 | 0.085470 | 0.538462 | -14.221517 | 79.517914 | 0.253762 |
| 9 | 10 | 0.400103 | 0.037527 | 1.194740 | 1.644877 | 0.071795 | 0.038991 | 0.098845 | 0.102905 | 0.119658 | 0.658120 | 19.474030 | 64.487673 | 0.274513 |
| 10 | 11 | 0.500257 | 0.034968 | 0.853386 | 1.486416 | 0.051282 | 0.036245 | 0.089322 | 0.089559 | 0.085470 | 0.743590 | -14.661407 | 48.641605 | 0.258890 |
| 11 | 12 | 0.599897 | 0.032826 | 1.286677 | 1.453240 | 0.077320 | 0.033914 | 0.087329 | 0.080317 | 0.128205 | 0.871795 | 28.667724 | 45.324025 | 0.289281 |
| 12 | 13 | 0.700051 | 0.030337 | 0.512032 | 1.318585 | 0.030769 | 0.031621 | 0.079237 | 0.073350 | 0.051282 | 0.923077 | -48.796844 | 31.858457 | 0.237285 |
| 13 | 14 | 0.799692 | 0.027243 | 0.171557 | 1.175667 | 0.010309 | 0.028810 | 0.070649 | 0.067800 | 0.017094 | 0.940171 | -82.844303 | 17.566655 | 0.149461 |
| 14 | 15 | 0.899846 | 0.023323 | 0.256016 | 1.073308 | 0.015385 | 0.025362 | 0.064498 | 0.063077 | 0.025641 | 0.965812 | -74.398422 | 7.330816 | 0.070184 |
| 15 | 16 | 1.000000 | 0.000814 | 0.341354 | 1.000000 | 0.020513 | 0.018417 | 0.060092 | 0.058604 | 0.034188 | 1.000000 | -65.864563 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.047582155997827585 RMSE: 0.21813334453454747 LogLoss: 0.18367906063856224 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.710903979776 Residual deviance: 2860.2503322636917 AIC: 2890.2503322636917 AUC: 0.7765854537342649 AUCPR: 0.29558560672119855 Gini: 0.5531709074685298 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2825197528333067:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7025.0 | 293.0 | 0.04 | (293.0/7318.0) |
| 1 | 1 | 263.0 | 205.0 | 0.562 | (263.0/468.0) |
| 2 | Total | 7288.0 | 498.0 | 0.0714 | (556.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.282520 | 0.424431 | 151.0 |
| 1 | max f2 | 0.060558 | 0.461084 | 237.0 |
| 2 | max f0point5 | 0.360973 | 0.419540 | 105.0 |
| 3 | max accuracy | 0.470543 | 0.940406 | 39.0 |
| 4 | max precision | 0.882160 | 1.000000 | 0.0 |
| 5 | max recall | 0.016243 | 1.000000 | 385.0 |
| 6 | max specificity | 0.882160 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.282520 | 0.386615 | 151.0 |
| 8 | max min_per_class_accuracy | 0.039855 | 0.698142 | 291.0 |
| 9 | max mean_per_class_accuracy | 0.057041 | 0.729137 | 243.0 |
| 10 | max tns | 0.882160 | 7318.000000 | 0.0 |
| 11 | max fns | 0.882160 | 467.000000 | 0.0 |
| 12 | max fps | 0.001168 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016243 | 468.000000 | 385.0 |
| 14 | max tnr | 0.882160 | 1.000000 | 0.0 |
| 15 | max fnr | 0.882160 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001168 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016243 | 1.000000 | 385.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.450006 | 8.318376 | 8.318376 | 0.500000 | 0.538642 | 0.500000 | 0.538642 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.407109 | 7.891793 | 8.105084 | 0.474359 | 0.425641 | 0.487179 | 0.482142 | 0.079060 | 0.162393 | 689.179268 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.384467 | 7.038626 | 7.749598 | 0.423077 | 0.395099 | 0.465812 | 0.453127 | 0.070513 | 0.232906 | 603.862590 | 674.959822 | 0.215825 |
| 3 | 4 | 0.040072 | 0.362007 | 7.038626 | 7.571855 | 0.423077 | 0.372498 | 0.455128 | 0.432970 | 0.070513 | 0.303419 | 603.862590 | 657.185514 | 0.280188 |
| 4 | 5 | 0.050090 | 0.335918 | 5.758876 | 7.209259 | 0.346154 | 0.349604 | 0.433333 | 0.416297 | 0.057692 | 0.361111 | 475.887574 | 620.925926 | 0.330912 |
| 5 | 6 | 0.100051 | 0.064346 | 2.950992 | 5.082859 | 0.177378 | 0.172233 | 0.305520 | 0.294421 | 0.147436 | 0.508547 | 195.099202 | 408.285880 | 0.434620 |
| 6 | 7 | 0.150013 | 0.050561 | 1.154736 | 3.774606 | 0.069409 | 0.055756 | 0.226884 | 0.214934 | 0.057692 | 0.566239 | 15.473601 | 277.460558 | 0.442845 |
| 7 | 8 | 0.200103 | 0.045508 | 0.853167 | 3.043308 | 0.051282 | 0.047627 | 0.182927 | 0.173054 | 0.042735 | 0.608974 | -14.683322 | 204.330832 | 0.435020 |
| 8 | 9 | 0.300026 | 0.040497 | 0.727056 | 2.271885 | 0.043702 | 0.042696 | 0.136558 | 0.129639 | 0.072650 | 0.681624 | -27.294399 | 127.188524 | 0.406002 |
| 9 | 10 | 0.400077 | 0.037312 | 0.427131 | 1.810549 | 0.025674 | 0.038932 | 0.108828 | 0.106955 | 0.042735 | 0.724359 | -57.286901 | 81.054863 | 0.345020 |
| 10 | 11 | 0.500000 | 0.034899 | 0.791208 | 1.606838 | 0.047558 | 0.036063 | 0.096584 | 0.092787 | 0.079060 | 0.803419 | -20.879199 | 60.683761 | 0.322823 |
| 11 | 12 | 0.600051 | 0.032509 | 0.640696 | 1.445745 | 0.038511 | 0.033673 | 0.086901 | 0.082931 | 0.064103 | 0.867521 | -35.930351 | 44.574516 | 0.284575 |
| 12 | 13 | 0.699974 | 0.030157 | 0.384912 | 1.294309 | 0.023136 | 0.031347 | 0.077798 | 0.075567 | 0.038462 | 0.905983 | -61.508800 | 29.430879 | 0.219183 |
| 13 | 14 | 0.800026 | 0.027375 | 0.341705 | 1.175176 | 0.020539 | 0.028814 | 0.070637 | 0.069720 | 0.034188 | 0.940171 | -65.829521 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.022755 | 0.342144 | 1.082683 | 0.020566 | 0.025393 | 0.065078 | 0.064798 | 0.034188 | 0.974359 | -65.785600 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.000875 | 0.256279 | 1.000000 | 0.015404 | 0.018075 | 0.060108 | 0.060124 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93694633 | 0.021063676 | 0.9307692 | 0.9115385 | 0.88461536 | 0.9461538 | 0.9346154 | 0.9461538 | 0.9115385 | 0.93846154 | 0.9423077 | 0.9653846 | 0.9269231 | 0.95384616 | 0.9576923 | 0.90384614 | 0.9461538 | 0.9269231 | 0.94208497 | 0.9189189 | 0.972973 | 0.93822396 | 0.9459459 | 0.9150579 | 0.95752895 | 0.93822396 | 0.9498069 | 0.969112 | 0.95752895 | 0.93436295 | 0.8996139 | 0.94208497 |
| 1 | auc | 0.7903703 | 0.0769474 | 0.88508254 | 0.7279614 | 0.7845558 | 0.8623585 | 0.826875 | 0.69076216 | 0.75 | 0.76785284 | 0.79435486 | 0.8445967 | 0.6752566 | 0.86760753 | 0.69512194 | 0.79123974 | 0.81678283 | 0.6714419 | 0.74859285 | 0.8665272 | 0.80533063 | 0.731305 | 0.8331028 | 0.65589744 | 0.86023337 | 0.84876543 | 0.8974359 | 0.96390015 | 0.77496624 | 0.68060285 | 0.7768557 | 0.81574345 |
| 2 | err | 0.06305366 | 0.021063676 | 0.06923077 | 0.08846154 | 0.115384616 | 0.053846154 | 0.06538462 | 0.053846154 | 0.08846154 | 0.06153846 | 0.057692308 | 0.034615386 | 0.073076926 | 0.046153847 | 0.042307694 | 0.09615385 | 0.053846154 | 0.073076926 | 0.057915058 | 0.08108108 | 0.027027028 | 0.06177606 | 0.054054055 | 0.08494209 | 0.042471044 | 0.06177606 | 0.05019305 | 0.03088803 | 0.042471044 | 0.06563707 | 0.1003861 | 0.057915058 |
| 3 | err_count | 16.366667 | 5.473972 | 18.0 | 23.0 | 30.0 | 14.0 | 17.0 | 14.0 | 23.0 | 16.0 | 15.0 | 9.0 | 19.0 | 12.0 | 11.0 | 25.0 | 14.0 | 19.0 | 15.0 | 21.0 | 7.0 | 16.0 | 14.0 | 22.0 | 11.0 | 16.0 | 13.0 | 8.0 | 11.0 | 17.0 | 26.0 | 15.0 |
| 4 | f0point5 | 0.49339083 | 0.117242485 | 0.39325842 | 0.3488372 | 0.33557048 | 0.4225352 | 0.5555556 | 0.64102566 | 0.45918366 | 0.5194805 | 0.3125 | 0.6666667 | 0.48192772 | 0.5147059 | 0.5882353 | 0.37878788 | 0.5 | 0.32467532 | 0.45454547 | 0.5147059 | 0.75 | 0.31746033 | 0.6097561 | 0.5504587 | 0.67901236 | 0.5113636 | 0.44444445 | 0.65789473 | 0.5208333 | 0.47945204 | 0.36885247 | 0.5 |
| 5 | f1 | 0.47827488 | 0.09918053 | 0.4375 | 0.34285715 | 0.4 | 0.46153846 | 0.4848485 | 0.41666666 | 0.4390244 | 0.5 | 0.2857143 | 0.5714286 | 0.45714286 | 0.53846157 | 0.42105263 | 0.44444445 | 0.5 | 0.3448276 | 0.4827586 | 0.5714286 | 0.6315789 | 0.33333334 | 0.5882353 | 0.5217391 | 0.6666667 | 0.5294118 | 0.3809524 | 0.71428573 | 0.47619048 | 0.4516129 | 0.4090909 | 0.54545456 |
| 6 | f2 | 0.47768098 | 0.113921545 | 0.49295774 | 0.33707866 | 0.4950495 | 0.5084746 | 0.43010753 | 0.30864197 | 0.42056075 | 0.48192772 | 0.2631579 | 0.5 | 0.4347826 | 0.5645161 | 0.32786885 | 0.53763443 | 0.5 | 0.36764705 | 0.5147059 | 0.64220184 | 0.54545456 | 0.3508772 | 0.5681818 | 0.49586776 | 0.6547619 | 0.5487805 | 0.33333334 | 0.78125 | 0.4385965 | 0.42682928 | 0.45918366 | 0.6 |
| 7 | lift_top_group | 8.509188 | 4.630508 | 0.0 | 4.814815 | 5.098039 | 7.878788 | 4.3333335 | 13.684211 | 3.939394 | 5.098039 | 0.0 | 20.0 | 9.122807 | 7.2222223 | 12.380953 | 10.833333 | 6.1904764 | 13.333333 | 6.6410255 | 8.633333 | 14.388889 | 0.0 | 9.592592 | 10.36 | 10.156863 | 10.791667 | 13.282051 | 7.1944447 | 14.388889 | 10.156863 | 9.592592 | 6.1666665 |
| 8 | logloss | 0.1823544 | 0.039204326 | 0.16103807 | 0.2300623 | 0.22482929 | 0.13597405 | 0.21584477 | 0.2396261 | 0.25102472 | 0.19341646 | 0.16972812 | 0.14586437 | 0.22539596 | 0.13990273 | 0.18684766 | 0.18840446 | 0.1622565 | 0.17423396 | 0.15531318 | 0.18625307 | 0.13007948 | 0.16010417 | 0.1824278 | 0.27689165 | 0.15962638 | 0.17320278 | 0.15893732 | 0.108394966 | 0.15092346 | 0.20418267 | 0.22046518 | 0.15938036 |
| 9 | max_per_class_error | 0.5160299 | 0.13531084 | 0.46153846 | 0.6666667 | 0.4117647 | 0.45454547 | 0.6 | 0.7368421 | 0.59090906 | 0.5294118 | 0.75 | 0.53846157 | 0.57894737 | 0.41666666 | 0.71428573 | 0.375 | 0.5 | 0.61538464 | 0.46153846 | 0.3 | 0.5 | 0.6363636 | 0.44444445 | 0.52 | 0.3529412 | 0.4375 | 0.6923077 | 0.16666667 | 0.5833333 | 0.5882353 | 0.5 | 0.35714287 |
| 10 | mcc | 0.4573952 | 0.10531725 | 0.41022608 | 0.2956147 | 0.3665386 | 0.43969297 | 0.46358633 | 0.49870834 | 0.39252582 | 0.46838892 | 0.25917596 | 0.57226336 | 0.4200517 | 0.51601464 | 0.46289775 | 0.41767776 | 0.4715447 | 0.30842358 | 0.455169 | 0.53945225 | 0.6428786 | 0.30233613 | 0.56052816 | 0.47769243 | 0.644346 | 0.49746263 | 0.3677857 | 0.7063939 | 0.45964268 | 0.4192773 | 0.3634155 | 0.5221443 |
| 11 | mean_per_class_accuracy | 0.72493684 | 0.06422741 | 0.74493927 | 0.6439394 | 0.74679255 | 0.754655 | 0.68958336 | 0.6315789 | 0.68353707 | 0.7208908 | 0.61290324 | 0.72672063 | 0.6939288 | 0.77755374 | 0.6408246 | 0.7735656 | 0.7357724 | 0.6700405 | 0.75093806 | 0.81861925 | 0.7479757 | 0.66367304 | 0.76532966 | 0.7207692 | 0.8131988 | 0.7627315 | 0.6457161 | 0.9045209 | 0.70023614 | 0.69141954 | 0.71473026 | 0.8010204 |
| 12 | mean_per_class_error | 0.2750632 | 0.06422741 | 0.25506073 | 0.3560606 | 0.25320745 | 0.24534501 | 0.31041667 | 0.36842105 | 0.31646293 | 0.27910918 | 0.38709676 | 0.27327934 | 0.3060712 | 0.22244623 | 0.35917538 | 0.22643442 | 0.26422763 | 0.3299595 | 0.24906191 | 0.18138075 | 0.2520243 | 0.336327 | 0.23467036 | 0.27923077 | 0.18680117 | 0.23726852 | 0.35428393 | 0.095479086 | 0.29976383 | 0.30858046 | 0.2852697 | 0.19897959 |
| 13 | mse | 0.04738383 | 0.011305485 | 0.043708086 | 0.06087546 | 0.059914146 | 0.034661245 | 0.058683053 | 0.0624684 | 0.068045095 | 0.05038913 | 0.042833123 | 0.03612356 | 0.05912249 | 0.03528961 | 0.04629935 | 0.048996028 | 0.04188868 | 0.043156642 | 0.038587157 | 0.05206783 | 0.031014912 | 0.038375493 | 0.04887095 | 0.07423694 | 0.04217744 | 0.045494452 | 0.041525703 | 0.027484301 | 0.036409754 | 0.05253811 | 0.057973865 | 0.042303883 |
| 14 | null_deviance | 118.0237 | 19.556776 | 103.75808 | 131.24835 | 125.73113 | 92.82863 | 142.31174 | 136.7752 | 153.41394 | 125.73113 | 98.28862 | 103.75808 | 136.7752 | 98.28862 | 109.23703 | 120.223526 | 109.23703 | 103.75808 | 103.63261 | 142.19029 | 98.16257 | 92.701996 | 131.12575 | 170.02197 | 125.607956 | 120.09978 | 103.63261 | 98.16257 | 98.16257 | 125.607956 | 131.12575 | 109.11213 |
| 15 | pr_auc | 0.3373594 | 0.10764318 | 0.24452738 | 0.21086445 | 0.21271788 | 0.22767784 | 0.32927552 | 0.35841522 | 0.2955839 | 0.307831 | 0.14472076 | 0.50920165 | 0.27933022 | 0.28564638 | 0.297556 | 0.3698041 | 0.30288735 | 0.27614313 | 0.33698937 | 0.40353036 | 0.5146813 | 0.15740924 | 0.49469236 | 0.44944492 | 0.5279966 | 0.43302223 | 0.36073747 | 0.542468 | 0.35073063 | 0.26322472 | 0.3043282 | 0.32934386 |
| 16 | precision | 0.5185624 | 0.17117463 | 0.36842105 | 0.3529412 | 0.3030303 | 0.4 | 0.61538464 | 1.0 | 0.47368422 | 0.53333336 | 0.33333334 | 0.75 | 0.5 | 0.5 | 0.8 | 0.3448276 | 0.5 | 0.3125 | 0.4375 | 0.4827586 | 0.85714287 | 0.30769232 | 0.625 | 0.5714286 | 0.6875 | 0.5 | 0.5 | 0.625 | 0.5555556 | 0.5 | 0.34615386 | 0.47368422 |
| 17 | r2 | 0.15962486 | 0.0852752 | 0.079829775 | 0.055284422 | 0.019560313 | 0.14454171 | 0.173547 | 0.07777598 | 0.12149569 | 0.17542842 | 0.027043281 | 0.23950404 | 0.12717175 | 0.19839457 | 0.09122064 | 0.15160564 | 0.17779475 | 0.09143914 | 0.19059877 | 0.2692966 | 0.2980731 | 0.056353927 | 0.24428025 | 0.14873706 | 0.31227398 | 0.21506858 | 0.12896007 | 0.37797758 | 0.17597751 | 0.1433376 | 0.103516646 | 0.17265697 |
| 18 | recall | 0.48397008 | 0.13531084 | 0.53846157 | 0.33333334 | 0.5882353 | 0.54545456 | 0.4 | 0.2631579 | 0.4090909 | 0.47058824 | 0.25 | 0.46153846 | 0.42105263 | 0.5833333 | 0.2857143 | 0.625 | 0.5 | 0.3846154 | 0.53846157 | 0.7 | 0.5 | 0.36363637 | 0.5555556 | 0.48 | 0.64705884 | 0.5625 | 0.30769232 | 0.8333333 | 0.41666666 | 0.4117647 | 0.5 | 0.64285713 |
| 19 | residual_deviance | 94.662544 | 20.387335 | 83.73979 | 119.6324 | 116.91123 | 70.706505 | 112.23927 | 124.60557 | 130.53285 | 100.57656 | 88.25862 | 75.84947 | 117.2059 | 72.74942 | 97.16078 | 97.97032 | 84.373375 | 90.601654 | 80.452225 | 96.47909 | 67.38117 | 82.93396 | 94.4976 | 143.42989 | 82.68646 | 89.71905 | 82.32954 | 56.148594 | 78.17835 | 105.766624 | 114.200966 | 82.55903 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:12:10 | 0.000 sec | 2 | .9E1 | 15.0 | 0.451965 | 0.452579 | 0.4523 | 0.013708 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:12:10 | 0.003 sec | 4 | .56E1 | 15.0 | 0.450437 | 0.451469 | 0.450836 | 0.013677 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:12:10 | 0.005 sec | 6 | .35E1 | 15.0 | 0.448033 | 0.449726 | 0.448531 | 0.013629 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:12:10 | 0.008 sec | 8 | .22E1 | 15.0 | 0.44429 | 0.447023 | 0.444939 | 0.013558 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:12:10 | 0.010 sec | 10 | .13E1 | 15.0 | 0.438636 | 0.442961 | 0.439499 | 0.013457 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:12:10 | 0.013 sec | 12 | .83E0 | 15.0 | 0.430453 | 0.437143 | 0.431596 | 0.01333 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:12:10 | 0.015 sec | 14 | .52E0 | 15.0 | 0.419482 | 0.429484 | 0.420932 | 0.013197 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:12:10 | 0.018 sec | 16 | .32E0 | 15.0 | 0.406525 | 0.42074 | 0.408213 | 0.013113 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:12:10 | 0.020 sec | 18 | .2E0 | 15.0 | 0.39368 | 0.412591 | 0.395461 | 0.013138 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:12:10 | 0.023 sec | 20 | .12E0 | 15.0 | 0.383149 | 0.406604 | 0.384934 | 0.013287 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:12:10 | 0.025 sec | 22 | .77E-1 | 15.0 | 0.375761 | 0.40312 | 0.377567 | 0.013511 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:12:10 | 0.028 sec | 24 | .48E-1 | 15.0 | 0.37106 | 0.401515 | 0.372964 | 0.013748 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:12:10 | 0.030 sec | 26 | .3E-1 | 15.0 | 0.36819 | 0.401 | 0.370262 | 0.013958 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:12:10 | 0.032 sec | 28 | .18E-1 | 15.0 | 0.366432 | 0.40103 | 0.368708 | 0.014126 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:12:10 | 0.035 sec | 30 | .11E-1 | 15.0 | 0.365338 | 0.401325 | 0.367821 | 0.014254 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:12:10 | 0.267 sec | 31 | None | NaN | 31.0 | 0.217442 | 0.182327 | 0.163093 | 0.78794 | 0.306713 | 8.744959 | 0.070768 | 0.226529 | 0.200877 | 0.091462 | 0.717302 | 0.223174 | 7.488462 | 0.076528 | ||||||
| 16 | 2021-07-15 20:12:10 | 0.037 sec | 32 | .71E-2 | 15.0 | 0.364654 | 0.401754 | 0.367326 | 0.014351 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:12:10 | 0.040 sec | 34 | .44E-2 | 15.0 | 0.364239 | 0.402239 | 0.369424 | 0.014765 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:12:10 | 0.042 sec | 36 | .27E-2 | 15.0 | 0.363999 | 0.402709 | 0.372689 | 0.01568 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:12:10 | 0.044 sec | 37 | .17E-2 | 15.0 | 0.363866 | 0.403117 | 0.376908 | 0.016509 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.574452 | 1.000000 | 0.256373 |
| 1 | Average_Transaction_Frequency | 0.272254 | 0.473937 | 0.121505 |
| 2 | Card_Type.1 | 0.207085 | 0.360492 | 0.092420 |
| 3 | Card_Type.0 | 0.204768 | 0.356457 | 0.091386 |
| 4 | Channel_ID | 0.198872 | 0.346194 | 0.088755 |
| 5 | Minimum_Transaction_Amount | 0.195613 | 0.340520 | 0.087300 |
| 6 | Merchant_ID | 0.182037 | 0.316887 | 0.081241 |
| 7 | Transaction_Amount | 0.119761 | 0.208479 | 0.053449 |
| 8 | Transaction_Date | 0.084439 | 0.146991 | 0.037684 |
| 9 | Maximum_Transaction_Amount | 0.082026 | 0.142790 | 0.036608 |
| 10 | Average_Transaction_Amount | 0.064406 | 0.112117 | 0.028744 |
| 11 | Month | 0.037838 | 0.065868 | 0.016887 |
| 12 | City_ID | 0.010415 | 0.018131 | 0.004648 |
| 13 | Day | 0.006722 | 0.011702 | 0.003000 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201213 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01066 ) | nlambda = 30, lambda.max = 8.4022, lambda.min = 0.01066, lambda.1s... | 14 | 14 | 30 | automl_training_py_192_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.048319996724750285 RMSE: 0.2198180991746364 LogLoss: 0.18726568340677022 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793836 Residual deviance: 2916.101222010225 AIC: 2946.101222010225 AUC: 0.7658129293651295 AUCPR: 0.2863485378613815 Gini: 0.5316258587302589 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2555018291249065:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7006.0 | 312.0 | 0.0426 | (312.0/7318.0) |
| 1 | 1 | 271.0 | 197.0 | 0.5791 | (271.0/468.0) |
| 2 | Total | 7277.0 | 509.0 | 0.0749 | (583.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.255502 | 0.403275 | 150.0 |
| 1 | max f2 | 0.072017 | 0.437548 | 220.0 |
| 2 | max f0point5 | 0.314478 | 0.410079 | 113.0 |
| 3 | max accuracy | 0.418918 | 0.940920 | 40.0 |
| 4 | max precision | 0.543721 | 0.769231 | 11.0 |
| 5 | max recall | 0.019862 | 1.000000 | 381.0 |
| 6 | max specificity | 0.837444 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.255502 | 0.363770 | 150.0 |
| 8 | max min_per_class_accuracy | 0.041892 | 0.683761 | 289.0 |
| 9 | max mean_per_class_accuracy | 0.061161 | 0.710956 | 234.0 |
| 10 | max tns | 0.837444 | 7317.000000 | 0.0 |
| 11 | max fns | 0.837444 | 468.000000 | 0.0 |
| 12 | max fps | 0.001395 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019862 | 468.000000 | 381.0 |
| 14 | max tnr | 0.837444 | 0.999863 | 0.0 |
| 15 | max fnr | 0.837444 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001395 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019862 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.409561 | 9.171543 | 9.171543 | 0.551282 | 0.480504 | 0.551282 | 0.480504 | 0.091880 | 0.091880 | 817.154284 | 817.154284 | 0.087098 |
| 1 | 2 | 0.020036 | 0.377196 | 7.038626 | 8.105084 | 0.423077 | 0.393413 | 0.487179 | 0.436959 | 0.070513 | 0.162393 | 603.862590 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.353482 | 6.612043 | 7.607404 | 0.397436 | 0.365916 | 0.457265 | 0.413278 | 0.066239 | 0.228632 | 561.204252 | 660.740375 | 0.211278 |
| 3 | 4 | 0.040072 | 0.334240 | 6.398751 | 7.305241 | 0.384615 | 0.343982 | 0.439103 | 0.395954 | 0.064103 | 0.292735 | 539.875082 | 630.524052 | 0.268821 |
| 4 | 5 | 0.050090 | 0.313689 | 6.185459 | 7.081284 | 0.371795 | 0.325171 | 0.425641 | 0.381797 | 0.061966 | 0.354701 | 518.545913 | 608.128424 | 0.324091 |
| 5 | 6 | 0.100051 | 0.066751 | 2.737152 | 4.912006 | 0.164524 | 0.165962 | 0.295250 | 0.274018 | 0.136752 | 0.491453 | 173.715202 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.052107 | 0.855360 | 3.560949 | 0.051414 | 0.057821 | 0.214041 | 0.202014 | 0.042735 | 0.534188 | -14.463999 | 256.094866 | 0.408744 |
| 7 | 8 | 0.200103 | 0.047880 | 1.066458 | 2.936526 | 0.064103 | 0.049802 | 0.176508 | 0.163912 | 0.053419 | 0.587607 | 6.645847 | 193.652557 | 0.412286 |
| 8 | 9 | 0.300026 | 0.043106 | 0.791208 | 2.222032 | 0.047558 | 0.045212 | 0.133562 | 0.124379 | 0.079060 | 0.666667 | -20.879199 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.039613 | 0.512557 | 1.794526 | 0.030809 | 0.041316 | 0.107865 | 0.103607 | 0.051282 | 0.717949 | -48.744281 | 79.452607 | 0.338200 |
| 10 | 11 | 0.500000 | 0.037104 | 0.705672 | 1.576923 | 0.042416 | 0.038359 | 0.094786 | 0.090567 | 0.070513 | 0.788462 | -29.432799 | 57.692308 | 0.306909 |
| 11 | 12 | 0.600051 | 0.034663 | 0.619340 | 1.417258 | 0.037227 | 0.035878 | 0.085188 | 0.081448 | 0.061966 | 0.850427 | -38.066006 | 41.725757 | 0.266388 |
| 12 | 13 | 0.699974 | 0.032297 | 0.449064 | 1.279046 | 0.026992 | 0.033464 | 0.076881 | 0.074598 | 0.044872 | 0.895299 | -55.093600 | 27.904571 | 0.207816 |
| 13 | 14 | 0.800026 | 0.029401 | 0.469844 | 1.177847 | 0.028241 | 0.030937 | 0.070798 | 0.069138 | 0.047009 | 0.942308 | -53.015591 | 17.784680 | 0.151381 |
| 14 | 15 | 0.899949 | 0.025546 | 0.320760 | 1.082683 | 0.019280 | 0.027587 | 0.065078 | 0.064525 | 0.032051 | 0.974359 | -67.924000 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.001054 | 0.256279 | 1.000000 | 0.015404 | 0.020380 | 0.060108 | 0.060108 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04763253002027534 RMSE: 0.2182487801117691 LogLoss: 0.1826888187134711 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311446 Residual deviance: 711.3902600702567 AIC: 741.3902600702567 AUC: 0.8069053290364766 AUCPR: 0.30168721426895256 Gini: 0.6138106580729532 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13241365117287016:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1753.0 | 77.0 | 0.0421 | (77.0/1830.0) |
| 1 | 1 | 63.0 | 54.0 | 0.5385 | (63.0/117.0) |
| 2 | Total | 1816.0 | 131.0 | 0.0719 | (140.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.132414 | 0.435484 | 108.0 |
| 1 | max f2 | 0.062579 | 0.468750 | 154.0 |
| 2 | max f0point5 | 0.300711 | 0.451056 | 80.0 |
| 3 | max accuracy | 0.357516 | 0.941962 | 39.0 |
| 4 | max precision | 0.357516 | 0.543478 | 39.0 |
| 5 | max recall | 0.020378 | 1.000000 | 373.0 |
| 6 | max specificity | 0.812262 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.300711 | 0.398859 | 80.0 |
| 8 | max min_per_class_accuracy | 0.043885 | 0.735043 | 228.0 |
| 9 | max mean_per_class_accuracy | 0.049115 | 0.737551 | 198.0 |
| 10 | max tns | 0.812262 | 1829.000000 | 0.0 |
| 11 | max fns | 0.812262 | 117.000000 | 0.0 |
| 12 | max fps | 0.001130 | 1830.000000 | 399.0 |
| 13 | max tps | 0.020378 | 117.000000 | 373.0 |
| 14 | max tnr | 0.812262 | 0.999454 | 0.0 |
| 15 | max fnr | 0.812262 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001130 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020378 | 1.000000 | 373.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.87 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.413561 | 7.488462 | 7.488462 | 0.450000 | 0.497163 | 0.450000 | 0.497163 | 0.076923 | 0.076923 | 648.846154 | 648.846154 | 0.070912 |
| 1 | 2 | 0.020031 | 0.365529 | 10.510121 | 8.960552 | 0.631579 | 0.385514 | 0.538462 | 0.442770 | 0.102564 | 0.179487 | 951.012146 | 796.055227 | 0.169651 |
| 2 | 3 | 0.030303 | 0.341134 | 8.320513 | 8.743590 | 0.500000 | 0.352382 | 0.525424 | 0.412130 | 0.085470 | 0.264957 | 732.051282 | 774.358974 | 0.249657 |
| 3 | 4 | 0.040062 | 0.325786 | 4.379217 | 7.680473 | 0.263158 | 0.332917 | 0.461538 | 0.392835 | 0.042735 | 0.307692 | 337.921727 | 668.047337 | 0.284741 |
| 4 | 5 | 0.050334 | 0.301663 | 7.488462 | 7.641287 | 0.450000 | 0.313392 | 0.459184 | 0.376622 | 0.076923 | 0.384615 | 648.846154 | 664.128728 | 0.355654 |
| 5 | 6 | 0.100154 | 0.063959 | 2.744911 | 5.205654 | 0.164948 | 0.139024 | 0.312821 | 0.258432 | 0.136752 | 0.521368 | 174.491145 | 420.565417 | 0.448143 |
| 6 | 7 | 0.149974 | 0.053174 | 0.686228 | 3.704338 | 0.041237 | 0.057270 | 0.222603 | 0.191608 | 0.034188 | 0.555556 | -31.377214 | 270.433790 | 0.431512 |
| 7 | 8 | 0.200308 | 0.047951 | 1.528257 | 3.157528 | 0.091837 | 0.050365 | 0.189744 | 0.156116 | 0.076923 | 0.632479 | 52.825746 | 215.752794 | 0.459801 |
| 8 | 9 | 0.299949 | 0.043477 | 1.029342 | 2.450562 | 0.061856 | 0.045544 | 0.147260 | 0.119385 | 0.102564 | 0.735043 | 2.934179 | 145.056200 | 0.462912 |
| 9 | 10 | 0.400103 | 0.040220 | 0.768047 | 2.029393 | 0.046154 | 0.041857 | 0.121951 | 0.099978 | 0.076923 | 0.811966 | -23.195266 | 102.939337 | 0.438195 |
| 10 | 11 | 0.500257 | 0.037512 | 0.341354 | 1.691439 | 0.020513 | 0.038796 | 0.101643 | 0.087729 | 0.034188 | 0.846154 | -65.864563 | 69.143895 | 0.368012 |
| 11 | 12 | 0.599897 | 0.034881 | 0.428892 | 1.481735 | 0.025773 | 0.036119 | 0.089041 | 0.079157 | 0.042735 | 0.888889 | -57.110759 | 48.173516 | 0.307468 |
| 12 | 13 | 0.700051 | 0.032186 | 0.512032 | 1.343003 | 0.030769 | 0.033511 | 0.080704 | 0.072626 | 0.051282 | 0.940171 | -48.796844 | 34.300280 | 0.255471 |
| 13 | 14 | 0.799692 | 0.029188 | 0.257335 | 1.207730 | 0.015464 | 0.030734 | 0.072575 | 0.067407 | 0.025641 | 0.965812 | -74.266455 | 20.773018 | 0.176741 |
| 14 | 15 | 0.899846 | 0.024939 | 0.170677 | 1.092305 | 0.010256 | 0.027433 | 0.065639 | 0.062958 | 0.017094 | 0.982906 | -82.932281 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.001130 | 0.170677 | 1.000000 | 0.010256 | 0.020104 | 0.060092 | 0.058666 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04859546948311134 RMSE: 0.22044380119003423 LogLoss: 0.1885227792146999 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3542.3017121723456 Residual deviance: 2935.676717931307 AIC: 2965.676717931307 AUC: 0.7546190694762709 AUCPR: 0.2739915232095708 Gini: 0.5092381389525418 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.23321978140782987:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6991.0 | 327.0 | 0.0447 | (327.0/7318.0) |
| 1 | 1 | 268.0 | 200.0 | 0.5726 | (268.0/468.0) |
| 2 | Total | 7259.0 | 527.0 | 0.0764 | (595.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.233220 | 0.402010 | 151.0 |
| 1 | max f2 | 0.066032 | 0.435926 | 223.0 |
| 2 | max f0point5 | 0.318521 | 0.399590 | 105.0 |
| 3 | max accuracy | 0.534098 | 0.940663 | 10.0 |
| 4 | max precision | 0.760805 | 0.750000 | 3.0 |
| 5 | max recall | 0.018581 | 1.000000 | 383.0 |
| 6 | max specificity | 0.846214 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.233220 | 0.362072 | 151.0 |
| 8 | max min_per_class_accuracy | 0.041629 | 0.678327 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.065414 | 0.710055 | 224.0 |
| 10 | max tns | 0.846214 | 7317.000000 | 0.0 |
| 11 | max fns | 0.846214 | 468.000000 | 0.0 |
| 12 | max fps | 0.001118 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018581 | 468.000000 | 383.0 |
| 14 | max tnr | 0.846214 | 0.999863 | 0.0 |
| 15 | max fnr | 0.846214 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001118 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018581 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.407383 | 8.105084 | 8.105084 | 0.487179 | 0.482530 | 0.487179 | 0.482530 | 0.081197 | 0.081197 | 710.508437 | 710.508437 | 0.075731 |
| 1 | 2 | 0.020036 | 0.376867 | 7.038626 | 7.571855 | 0.423077 | 0.392689 | 0.455128 | 0.437610 | 0.070513 | 0.151709 | 603.862590 | 657.185514 | 0.140094 |
| 2 | 3 | 0.030054 | 0.353009 | 6.825334 | 7.323015 | 0.410256 | 0.363901 | 0.440171 | 0.413040 | 0.068376 | 0.220085 | 582.533421 | 632.301483 | 0.202184 |
| 3 | 4 | 0.040072 | 0.332540 | 5.972167 | 6.985303 | 0.358974 | 0.342665 | 0.419872 | 0.395446 | 0.059829 | 0.279915 | 497.216743 | 598.530298 | 0.255181 |
| 4 | 5 | 0.050090 | 0.312704 | 6.398751 | 6.867993 | 0.384615 | 0.323432 | 0.412821 | 0.381043 | 0.064103 | 0.344017 | 539.875082 | 586.799255 | 0.312724 |
| 5 | 6 | 0.100051 | 0.066239 | 2.908224 | 4.890650 | 0.174807 | 0.165324 | 0.293967 | 0.273322 | 0.145299 | 0.489316 | 190.822402 | 389.064986 | 0.414159 |
| 6 | 7 | 0.150013 | 0.052195 | 0.812592 | 3.532461 | 0.048843 | 0.057816 | 0.212329 | 0.201548 | 0.040598 | 0.529915 | -18.740799 | 253.246107 | 0.404197 |
| 7 | 8 | 0.200103 | 0.047951 | 0.682533 | 2.819065 | 0.041026 | 0.049791 | 0.169448 | 0.163560 | 0.034188 | 0.564103 | -31.746658 | 181.906455 | 0.387278 |
| 8 | 9 | 0.300026 | 0.043085 | 0.898128 | 2.179301 | 0.053985 | 0.045264 | 0.130993 | 0.124162 | 0.089744 | 0.653846 | -10.187199 | 117.930058 | 0.376448 |
| 9 | 10 | 0.400077 | 0.039732 | 0.576627 | 1.778504 | 0.034660 | 0.041365 | 0.106902 | 0.103456 | 0.057692 | 0.711538 | -42.337316 | 77.850352 | 0.331380 |
| 10 | 11 | 0.500000 | 0.037245 | 0.620136 | 1.547009 | 0.037275 | 0.038435 | 0.092987 | 0.090462 | 0.061966 | 0.773504 | -37.986399 | 54.700855 | 0.290995 |
| 11 | 12 | 0.600051 | 0.034707 | 0.597983 | 1.388770 | 0.035944 | 0.035982 | 0.083476 | 0.081378 | 0.059829 | 0.833333 | -40.201661 | 38.876998 | 0.248201 |
| 12 | 13 | 0.699974 | 0.032332 | 0.534600 | 1.266835 | 0.032134 | 0.033523 | 0.076147 | 0.074547 | 0.053419 | 0.886752 | -46.540000 | 26.683525 | 0.198723 |
| 13 | 14 | 0.800026 | 0.029462 | 0.405774 | 1.159151 | 0.024390 | 0.031005 | 0.069674 | 0.069101 | 0.040598 | 0.927350 | -59.422556 | 15.915082 | 0.135467 |
| 14 | 15 | 0.899949 | 0.025494 | 0.449064 | 1.080309 | 0.026992 | 0.027675 | 0.064935 | 0.064502 | 0.044872 | 0.972222 | -55.093600 | 8.030858 | 0.076896 |
| 15 | 16 | 1.000000 | 0.000948 | 0.277635 | 1.000000 | 0.016688 | 0.020486 | 0.060108 | 0.060098 | 0.027778 | 1.000000 | -72.236486 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93347937 | 0.02092834 | 0.9230769 | 0.9307692 | 0.9346154 | 0.9307692 | 0.9346154 | 0.9153846 | 0.89615387 | 0.95 | 0.9269231 | 0.9307692 | 0.9153846 | 0.8923077 | 0.9230769 | 0.9307692 | 0.97307694 | 0.9576923 | 0.96138996 | 0.93822396 | 0.9459459 | 0.9266409 | 0.93436295 | 0.90733594 | 0.8918919 | 0.969112 | 0.94208497 | 0.95366794 | 0.9227799 | 0.93822396 | 0.9459459 | 0.96138996 |
| 1 | auc | 0.7592968 | 0.06307501 | 0.7904762 | 0.6708504 | 0.70857143 | 0.78503996 | 0.7993029 | 0.6546247 | 0.7245763 | 0.7528295 | 0.71543163 | 0.7987805 | 0.7399364 | 0.7860943 | 0.79683197 | 0.7362259 | 0.77132934 | 0.8457661 | 0.78386456 | 0.7535519 | 0.6442623 | 0.8252651 | 0.7120614 | 0.615722 | 0.75034577 | 0.8244445 | 0.88251364 | 0.86108786 | 0.81688887 | 0.76071924 | 0.7402159 | 0.73129255 |
| 2 | err | 0.06652064 | 0.02092834 | 0.07692308 | 0.06923077 | 0.06538462 | 0.06923077 | 0.06538462 | 0.08461539 | 0.103846155 | 0.05 | 0.073076926 | 0.06923077 | 0.08461539 | 0.10769231 | 0.07692308 | 0.06923077 | 0.026923077 | 0.042307694 | 0.038610037 | 0.06177606 | 0.054054055 | 0.07335907 | 0.06563707 | 0.09266409 | 0.10810811 | 0.03088803 | 0.057915058 | 0.046332046 | 0.077220075 | 0.06177606 | 0.054054055 | 0.038610037 |
| 3 | err_count | 17.266666 | 5.4388976 | 20.0 | 18.0 | 17.0 | 18.0 | 17.0 | 22.0 | 27.0 | 13.0 | 19.0 | 18.0 | 22.0 | 28.0 | 20.0 | 18.0 | 7.0 | 11.0 | 10.0 | 16.0 | 14.0 | 19.0 | 17.0 | 24.0 | 28.0 | 8.0 | 15.0 | 12.0 | 20.0 | 16.0 | 14.0 | 10.0 |
| 4 | f0point5 | 0.44741422 | 0.11182315 | 0.3846154 | 0.390625 | 0.3968254 | 0.45454547 | 0.63106793 | 0.30927834 | 0.43103448 | 0.4477612 | 0.5405405 | 0.3846154 | 0.53571427 | 0.43165466 | 0.42682928 | 0.47297296 | 0.53571427 | 0.5625 | 0.3125 | 0.46666667 | 0.4651163 | 0.5 | 0.5421687 | 0.2173913 | 0.34615386 | 0.5405405 | 0.5063291 | 0.7236842 | 0.23529412 | 0.5405405 | 0.3409091 | 0.3488372 |
| 5 | f1 | 0.43813035 | 0.09809022 | 0.4117647 | 0.35714287 | 0.37037036 | 0.4375 | 0.60465115 | 0.3529412 | 0.42553192 | 0.48 | 0.45714286 | 0.4 | 0.5217391 | 0.46153846 | 0.4117647 | 0.4375 | 0.46153846 | 0.62068963 | 0.2857143 | 0.46666667 | 0.36363637 | 0.5365854 | 0.51428574 | 0.25 | 0.39130434 | 0.5 | 0.516129 | 0.64705884 | 0.2857143 | 0.5 | 0.3 | 0.375 |
| 6 | f2 | 0.43693888 | 0.0995616 | 0.443038 | 0.32894737 | 0.3472222 | 0.42168674 | 0.58035713 | 0.41095892 | 0.42016807 | 0.51724136 | 0.3960396 | 0.41666666 | 0.5084746 | 0.49586776 | 0.39772728 | 0.40697673 | 0.4054054 | 0.6923077 | 0.2631579 | 0.46666667 | 0.29850745 | 0.57894737 | 0.48913044 | 0.29411766 | 0.45 | 0.4651163 | 0.5263158 | 0.5851064 | 0.36363637 | 0.4651163 | 0.26785713 | 0.4054054 |
| 7 | lift_top_group | 6.9545355 | 5.2859025 | 11.555555 | 0.0 | 11.555555 | 10.196078 | 7.536232 | 6.6666665 | 10.833333 | 7.878788 | 7.878788 | 0.0 | 7.2222223 | 7.536232 | 0.0 | 9.62963 | 10.833333 | 14.444445 | 0.0 | 0.0 | 11.511111 | 9.592592 | 9.087719 | 0.0 | 9.592592 | 19.185184 | 5.7555556 | 12.95 | 0.0 | 0.0 | 7.1944447 | 0.0 |
| 8 | logloss | 0.18732397 | 0.042714626 | 0.17810002 | 0.21123531 | 0.19125329 | 0.19798286 | 0.22125362 | 0.17802165 | 0.26683447 | 0.1389392 | 0.25016013 | 0.18107976 | 0.2511642 | 0.2577261 | 0.21126145 | 0.21364632 | 0.11744943 | 0.12605508 | 0.13144916 | 0.17927106 | 0.19546723 | 0.18793513 | 0.21920072 | 0.18111861 | 0.22181278 | 0.11629478 | 0.16267715 | 0.19416727 | 0.13512428 | 0.20906058 | 0.17575628 | 0.118221134 |
| 9 | max_per_class_error | 0.55911446 | 0.109555244 | 0.53333336 | 0.6875 | 0.6666667 | 0.5882353 | 0.4347826 | 0.53846157 | 0.5833333 | 0.45454547 | 0.6363636 | 0.5714286 | 0.5 | 0.47826087 | 0.6111111 | 0.6111111 | 0.625 | 0.25 | 0.75 | 0.53333336 | 0.73333335 | 0.3888889 | 0.5263158 | 0.6666667 | 0.5 | 0.5555556 | 0.46666667 | 0.45 | 0.5555556 | 0.5555556 | 0.75 | 0.5714286 |
| 10 | mcc | 0.40950784 | 0.09956537 | 0.3741919 | 0.32506365 | 0.338667 | 0.40165952 | 0.5708528 | 0.32058987 | 0.3685787 | 0.45778096 | 0.4375246 | 0.364351 | 0.47596338 | 0.4059331 | 0.37150094 | 0.404828 | 0.4615634 | 0.6091962 | 0.26920313 | 0.4338798 | 0.3664037 | 0.5018042 | 0.48140234 | 0.21147548 | 0.34491238 | 0.48839623 | 0.4856482 | 0.6344583 | 0.27004892 | 0.47182348 | 0.27914262 | 0.35839146 |
| 11 | mean_per_class_accuracy | 0.7026329 | 0.052994158 | 0.7088435 | 0.6419057 | 0.65238094 | 0.6894215 | 0.76784074 | 0.7004049 | 0.68079096 | 0.756663 | 0.671314 | 0.6939605 | 0.7288136 | 0.7250046 | 0.6758494 | 0.67998165 | 0.68353176 | 0.858871 | 0.6170319 | 0.71693987 | 0.6271858 | 0.7806593 | 0.72225875 | 0.634278 | 0.7105809 | 0.7162222 | 0.7502732 | 0.76872385 | 0.69222224 | 0.7097741 | 0.61487854 | 0.70238096 |
| 12 | mean_per_class_error | 0.2973671 | 0.052994158 | 0.29115647 | 0.35809427 | 0.34761906 | 0.31057855 | 0.23215924 | 0.29959515 | 0.31920904 | 0.24333699 | 0.32868603 | 0.30603948 | 0.27118644 | 0.27499542 | 0.3241506 | 0.32001835 | 0.31646827 | 0.14112903 | 0.38296813 | 0.2830601 | 0.3728142 | 0.21934071 | 0.27774122 | 0.365722 | 0.28941908 | 0.28377777 | 0.24972677 | 0.23127615 | 0.3077778 | 0.29022592 | 0.38512146 | 0.29761904 |
| 13 | mse | 0.048217487 | 0.013104082 | 0.045808595 | 0.053861544 | 0.048335698 | 0.05191388 | 0.060126767 | 0.04442633 | 0.07133367 | 0.0339245 | 0.06688871 | 0.047111657 | 0.06794991 | 0.07133728 | 0.05653212 | 0.055251077 | 0.02597879 | 0.030186154 | 0.031093601 | 0.046252176 | 0.049293537 | 0.05053458 | 0.056915633 | 0.043530107 | 0.058719177 | 0.026801221 | 0.04322649 | 0.052226063 | 0.033197474 | 0.055486917 | 0.042375896 | 0.025905052 |
| 14 | null_deviance | 118.07672 | 27.066494 | 114.7255 | 120.223526 | 114.7255 | 125.73113 | 158.97968 | 103.75808 | 164.55519 | 92.82863 | 153.41394 | 109.23703 | 164.55519 | 158.97968 | 131.24835 | 131.24835 | 76.50513 | 98.28862 | 76.37677 | 114.60118 | 114.60118 | 131.12575 | 136.65318 | 98.16257 | 131.12575 | 81.80913 | 114.60118 | 142.19029 | 81.80913 | 131.12575 | 98.16257 | 70.953766 |
| 15 | pr_auc | 0.29063237 | 0.11259726 | 0.3344988 | 0.16936088 | 0.28345332 | 0.30813968 | 0.50492585 | 0.1511828 | 0.3664832 | 0.24365254 | 0.3509041 | 0.23859061 | 0.37957564 | 0.33098206 | 0.24681732 | 0.29187897 | 0.29877374 | 0.44288903 | 0.12323779 | 0.23804942 | 0.31268886 | 0.41988397 | 0.33263874 | 0.08412481 | 0.29386276 | 0.34459698 | 0.32782817 | 0.5688266 | 0.12530121 | 0.26827714 | 0.21714191 | 0.12040443 |
| 16 | precision | 0.45883608 | 0.13021897 | 0.36842105 | 0.41666666 | 0.41666666 | 0.46666667 | 0.65 | 0.2857143 | 0.4347826 | 0.42857143 | 0.61538464 | 0.375 | 0.54545456 | 0.41379312 | 0.4375 | 0.5 | 0.6 | 0.5294118 | 0.33333334 | 0.46666667 | 0.5714286 | 0.47826087 | 0.5625 | 0.2 | 0.32142857 | 0.5714286 | 0.5 | 0.78571427 | 0.21052632 | 0.5714286 | 0.375 | 0.33333334 |
| 17 | r2 | 0.13076763 | 0.080246195 | 0.15737118 | 0.0673565 | 0.11088621 | 0.15047729 | 0.25434425 | 0.06470886 | 0.1486306 | 0.16272499 | 0.13642538 | 0.07527641 | 0.18901585 | 0.11531824 | 0.12268794 | 0.14256822 | 0.1288858 | 0.31431988 | -0.03873996 | 0.15228356 | 0.096541055 | 0.2185546 | 0.1627286 | 0.014830274 | 0.09199146 | 0.20095435 | 0.20773874 | 0.26707608 | 0.010257883 | 0.14197372 | 0.04095227 | 0.014888438 |
| 18 | recall | 0.44088554 | 0.109555244 | 0.46666667 | 0.3125 | 0.33333334 | 0.4117647 | 0.5652174 | 0.46153846 | 0.41666666 | 0.54545456 | 0.36363637 | 0.42857143 | 0.5 | 0.5217391 | 0.3888889 | 0.3888889 | 0.375 | 0.75 | 0.25 | 0.46666667 | 0.26666668 | 0.6111111 | 0.47368422 | 0.33333334 | 0.5 | 0.44444445 | 0.53333336 | 0.55 | 0.44444445 | 0.44444445 | 0.25 | 0.42857143 |
| 19 | residual_deviance | 97.24663 | 22.240139 | 92.612015 | 109.84236 | 99.45171 | 102.95109 | 115.05188 | 92.57126 | 138.75392 | 72.24838 | 130.08327 | 94.161476 | 130.6054 | 134.01756 | 109.85595 | 111.096085 | 61.073704 | 65.548645 | 68.09067 | 92.86241 | 101.25203 | 97.350395 | 113.54597 | 93.819435 | 114.899025 | 60.240696 | 84.26676 | 100.57864 | 69.99438 | 108.29338 | 91.04175 | 61.23855 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:12:22 | 0.000 sec | 2 | .84E1 | 15.0 | 0.452158 | 0.451893 | 0.45263 | 0.018854 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:12:22 | 0.002 sec | 4 | .52E1 | 15.0 | 0.450747 | 0.450373 | 0.451279 | 0.018777 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:12:22 | 0.005 sec | 6 | .32E1 | 15.0 | 0.448529 | 0.447982 | 0.449156 | 0.018655 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:12:22 | 0.008 sec | 8 | .2E1 | 15.0 | 0.445083 | 0.444263 | 0.445851 | 0.018468 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:12:22 | 0.011 sec | 10 | .12E1 | 15.0 | 0.439893 | 0.43865 | 0.440862 | 0.018188 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:12:22 | 0.020 sec | 12 | .78E0 | 15.0 | 0.432419 | 0.430541 | 0.433646 | 0.017792 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:12:22 | 0.023 sec | 14 | .48E0 | 15.0 | 0.422471 | 0.41969 | 0.423973 | 0.017277 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:12:22 | 0.026 sec | 16 | .3E0 | 15.0 | 0.410836 | 0.406888 | 0.412543 | 0.016701 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:12:22 | 0.028 sec | 18 | .19E0 | 15.0 | 0.399417 | 0.394156 | 0.401194 | 0.016176 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:12:22 | 0.031 sec | 20 | .12E0 | 15.0 | 0.390134 | 0.383609 | 0.391909 | 0.015803 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:12:22 | 0.033 sec | 22 | .72E-1 | 15.0 | 0.383644 | 0.376056 | 0.385446 | 0.015596 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:12:22 | 0.036 sec | 24 | .45E-1 | 15.0 | 0.379514 | 0.371148 | 0.381418 | 0.015513 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:12:22 | 0.041 sec | 26 | .28E-1 | 15.0 | 0.376993 | 0.368137 | 0.379063 | 0.0155 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:12:22 | 0.044 sec | 28 | .17E-1 | 15.0 | 0.375459 | 0.366364 | 0.377728 | 0.015521 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:12:22 | 0.332 sec | 29 | None | NaN | 29.0 | 0.219818 | 0.187266 | 0.144702 | 0.765813 | 0.286349 | 9.171543 | 0.074878 | 0.218249 | 0.182689 | 0.156668 | 0.806905 | 0.301687 | 7.488462 | 0.071905 | ||||||
| 15 | 2021-07-15 20:12:22 | 0.047 sec | 30 | .11E-1 | 15.0 | 0.374531 | 0.365378 | 0.376996 | 0.015553 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:12:22 | 0.049 sec | 32 | .66E-2 | 15.0 | 0.373981 | 0.364869 | 0.377006 | 0.015584 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:12:22 | 0.052 sec | 34 | .41E-2 | 15.0 | 0.37367 | 0.364636 | 0.378772 | 0.015605 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:12:22 | 0.054 sec | 36 | .26E-2 | 15.0 | 0.373505 | 0.364543 | 0.380187 | 0.015765 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.543757 | 1.000000 | 0.272385 |
| 1 | Average_Transaction_Frequency | 0.256020 | 0.470834 | 0.128248 |
| 2 | Merchant_ID | 0.196040 | 0.360529 | 0.098203 |
| 3 | Minimum_Transaction_Amount | 0.187470 | 0.344767 | 0.093909 |
| 4 | Channel_ID | 0.167479 | 0.308003 | 0.083895 |
| 5 | Card_Type.1 | 0.126781 | 0.233157 | 0.063508 |
| 6 | Card_Type.0 | 0.125412 | 0.230639 | 0.062823 |
| 7 | Transaction_Amount | 0.115071 | 0.211622 | 0.057643 |
| 8 | Maximum_Transaction_Amount | 0.077582 | 0.142677 | 0.038863 |
| 9 | Transaction_Date | 0.066076 | 0.121518 | 0.033100 |
| 10 | Day | 0.053710 | 0.098776 | 0.026905 |
| 11 | Month | 0.053261 | 0.097950 | 0.026680 |
| 12 | Average_Transaction_Amount | 0.020028 | 0.036833 | 0.010033 |
| 13 | City_ID | 0.007597 | 0.013971 | 0.003806 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201225 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01086 ) | nlambda = 30, lambda.max = 8.5543, lambda.min = 0.01086, lambda.1s... | 14 | 14 | 30 | automl_training_py_221_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04831340703120522 RMSE: 0.2198031096941197 LogLoss: 0.18644530245008056 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793826 Residual deviance: 2903.326249752654 AIC: 2933.326249752654 AUC: 0.7741733589813666 AUCPR: 0.28333402239387906 Gini: 0.5483467179627333 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2056360751306347:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6974.0 | 344.0 | 0.047 | (344.0/7318.0) |
| 1 | 1 | 261.0 | 207.0 | 0.5577 | (261.0/468.0) |
| 2 | Total | 7235.0 | 551.0 | 0.0777 | (605.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.205636 | 0.406281 | 168.0 |
| 1 | max f2 | 0.060771 | 0.448087 | 230.0 |
| 2 | max f0point5 | 0.324832 | 0.402632 | 109.0 |
| 3 | max accuracy | 0.406806 | 0.940277 | 48.0 |
| 4 | max precision | 0.631280 | 0.666667 | 4.0 |
| 5 | max recall | 0.018909 | 1.000000 | 383.0 |
| 6 | max specificity | 0.826261 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.205636 | 0.366396 | 168.0 |
| 8 | max min_per_class_accuracy | 0.041703 | 0.690171 | 284.0 |
| 9 | max mean_per_class_accuracy | 0.060074 | 0.720229 | 231.0 |
| 10 | max tns | 0.826261 | 7317.000000 | 0.0 |
| 11 | max fns | 0.826261 | 467.000000 | 0.0 |
| 12 | max fps | 0.002568 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018909 | 468.000000 | 383.0 |
| 14 | max tnr | 0.826261 | 0.999863 | 0.0 |
| 15 | max fnr | 0.826261 | 0.997863 | 0.0 |
| 16 | max fpr | 0.002568 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018909 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.416029 | 8.318376 | 8.318376 | 0.500000 | 0.485476 | 0.500000 | 0.485476 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.377824 | 7.465209 | 7.891793 | 0.448718 | 0.394716 | 0.474359 | 0.440096 | 0.074786 | 0.158120 | 646.520929 | 689.179268 | 0.146914 |
| 2 | 3 | 0.030054 | 0.358391 | 6.185459 | 7.323015 | 0.371795 | 0.368764 | 0.440171 | 0.416319 | 0.061966 | 0.220085 | 518.545913 | 632.301483 | 0.202184 |
| 3 | 4 | 0.040072 | 0.338688 | 6.612043 | 7.145272 | 0.397436 | 0.347423 | 0.429487 | 0.399095 | 0.066239 | 0.286325 | 561.204252 | 614.527175 | 0.262001 |
| 4 | 5 | 0.050090 | 0.316707 | 5.119001 | 6.740018 | 0.307692 | 0.326788 | 0.405128 | 0.384633 | 0.051282 | 0.337607 | 411.900066 | 574.001753 | 0.305904 |
| 5 | 6 | 0.100051 | 0.065744 | 3.293136 | 5.018789 | 0.197943 | 0.170266 | 0.301669 | 0.277587 | 0.164530 | 0.502137 | 229.313603 | 401.878916 | 0.427800 |
| 6 | 7 | 0.150013 | 0.052286 | 0.812592 | 3.617924 | 0.048843 | 0.057516 | 0.217466 | 0.204293 | 0.040598 | 0.542735 | -18.740799 | 261.792384 | 0.417838 |
| 7 | 8 | 0.200103 | 0.047449 | 1.279750 | 3.032630 | 0.076923 | 0.049541 | 0.182285 | 0.165555 | 0.064103 | 0.606838 | 27.975016 | 203.263004 | 0.432746 |
| 8 | 9 | 0.300026 | 0.042769 | 0.727056 | 2.264763 | 0.043702 | 0.044837 | 0.136130 | 0.125350 | 0.072650 | 0.679487 | -27.294399 | 126.476335 | 0.403729 |
| 9 | 10 | 0.400077 | 0.039394 | 0.533914 | 1.831912 | 0.032092 | 0.041007 | 0.110112 | 0.104258 | 0.053419 | 0.732906 | -46.608626 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.036560 | 0.684288 | 1.602564 | 0.041131 | 0.037888 | 0.096327 | 0.090994 | 0.068376 | 0.801282 | -31.571199 | 60.256410 | 0.320550 |
| 11 | 12 | 0.600051 | 0.033925 | 0.555270 | 1.427940 | 0.033376 | 0.035225 | 0.085830 | 0.081695 | 0.055556 | 0.856838 | -44.472971 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.031507 | 0.555984 | 1.303467 | 0.033419 | 0.032655 | 0.078349 | 0.074695 | 0.055556 | 0.912393 | -44.401600 | 30.346664 | 0.226003 |
| 13 | 14 | 0.800026 | 0.028827 | 0.384418 | 1.188530 | 0.023107 | 0.030217 | 0.071440 | 0.069132 | 0.038462 | 0.950855 | -61.558211 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.025212 | 0.277992 | 1.087431 | 0.016710 | 0.027134 | 0.065363 | 0.064469 | 0.027778 | 0.978632 | -72.200800 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.002068 | 0.213565 | 1.000000 | 0.012837 | 0.020880 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.047765826640549555 RMSE: 0.21855394446348836 LogLoss: 0.18633736786978974 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.801798631156 Residual deviance: 725.5977104849613 AIC: 755.5977104849613 AUC: 0.7656601746765681 AUCPR: 0.2986804064932628 Gini: 0.5313203493531362 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2951989911890258:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1777.0 | 53.0 | 0.029 | (53.0/1830.0) |
| 1 | 1 | 68.0 | 49.0 | 0.5812 | (68.0/117.0) |
| 2 | Total | 1845.0 | 102.0 | 0.0621 | (121.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.295199 | 0.447489 | 88.0 |
| 1 | max f2 | 0.129694 | 0.431894 | 116.0 |
| 2 | max f0point5 | 0.295199 | 0.466667 | 88.0 |
| 3 | max accuracy | 0.469228 | 0.941962 | 9.0 |
| 4 | max precision | 0.855989 | 1.000000 | 0.0 |
| 5 | max recall | 0.020809 | 1.000000 | 374.0 |
| 6 | max specificity | 0.855989 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.295199 | 0.415823 | 88.0 |
| 8 | max min_per_class_accuracy | 0.041400 | 0.690164 | 236.0 |
| 9 | max mean_per_class_accuracy | 0.088174 | 0.701086 | 127.0 |
| 10 | max tns | 0.855989 | 1830.000000 | 0.0 |
| 11 | max fns | 0.855989 | 116.000000 | 0.0 |
| 12 | max fps | 0.002279 | 1830.000000 | 399.0 |
| 13 | max tps | 0.020809 | 117.000000 | 374.0 |
| 14 | max tnr | 0.855989 | 1.000000 | 0.0 |
| 15 | max fnr | 0.855989 | 0.991453 | 0.0 |
| 16 | max fpr | 0.002279 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020809 | 1.000000 | 374.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.82 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.419832 | 9.152564 | 9.152564 | 0.550000 | 0.510026 | 0.550000 | 0.510026 | 0.094017 | 0.094017 | 815.256410 | 815.256410 | 0.089099 |
| 1 | 2 | 0.020031 | 0.378731 | 7.006748 | 8.107166 | 0.421053 | 0.398804 | 0.487179 | 0.455841 | 0.068376 | 0.162393 | 600.674764 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.347301 | 7.488462 | 7.897436 | 0.450000 | 0.363853 | 0.474576 | 0.424658 | 0.076923 | 0.239316 | 648.846154 | 689.743590 | 0.222376 |
| 3 | 4 | 0.040062 | 0.329561 | 8.758435 | 8.107166 | 0.526316 | 0.337881 | 0.487179 | 0.403520 | 0.085470 | 0.324786 | 775.843455 | 710.716634 | 0.302928 |
| 4 | 5 | 0.050334 | 0.298246 | 6.656410 | 7.811094 | 0.400000 | 0.315147 | 0.469388 | 0.385485 | 0.068376 | 0.393162 | 565.641026 | 681.109367 | 0.364747 |
| 5 | 6 | 0.100154 | 0.059412 | 1.544013 | 4.693623 | 0.092784 | 0.136194 | 0.282051 | 0.261479 | 0.076923 | 0.470085 | 54.401269 | 369.362262 | 0.393583 |
| 6 | 7 | 0.149974 | 0.050901 | 0.857785 | 3.419389 | 0.051546 | 0.054298 | 0.205479 | 0.192655 | 0.042735 | 0.512821 | -14.221517 | 241.938883 | 0.386045 |
| 7 | 8 | 0.200308 | 0.046936 | 1.018838 | 2.816174 | 0.061224 | 0.048776 | 0.169231 | 0.156501 | 0.051282 | 0.564103 | 1.883830 | 181.617357 | 0.387053 |
| 8 | 9 | 0.299949 | 0.042542 | 0.943563 | 2.194108 | 0.056701 | 0.044663 | 0.131849 | 0.119350 | 0.094017 | 0.658120 | -5.643669 | 119.410783 | 0.381070 |
| 9 | 10 | 0.400103 | 0.039189 | 0.682709 | 1.815773 | 0.041026 | 0.040798 | 0.109114 | 0.099686 | 0.068376 | 0.726496 | -31.729126 | 81.577302 | 0.347261 |
| 10 | 11 | 0.500257 | 0.036399 | 0.853386 | 1.623098 | 0.051282 | 0.037729 | 0.097536 | 0.087282 | 0.085470 | 0.811966 | -14.661407 | 62.309798 | 0.331638 |
| 11 | 12 | 0.599897 | 0.033897 | 0.686228 | 1.467488 | 0.041237 | 0.035111 | 0.088185 | 0.078617 | 0.068376 | 0.880342 | -31.377214 | 46.748771 | 0.298375 |
| 12 | 13 | 0.700051 | 0.031501 | 0.170677 | 1.281957 | 0.010256 | 0.032695 | 0.077036 | 0.072047 | 0.017094 | 0.897436 | -82.932281 | 28.195722 | 0.210004 |
| 13 | 14 | 0.799692 | 0.028706 | 0.428892 | 1.175667 | 0.025773 | 0.030167 | 0.070649 | 0.066829 | 0.042735 | 0.940171 | -57.110759 | 17.566655 | 0.149461 |
| 14 | 15 | 0.899846 | 0.024754 | 0.426693 | 1.092305 | 0.025641 | 0.026866 | 0.065639 | 0.062381 | 0.042735 | 0.982906 | -57.330703 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.002119 | 0.170677 | 1.000000 | 0.010256 | 0.020224 | 0.060092 | 0.058159 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048678589920166826 RMSE: 0.22063225040815504 LogLoss: 0.1880595728069557 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.5666169971655 Residual deviance: 2928.4636677499143 AIC: 2958.4636677499143 AUC: 0.7606888704353859 AUCPR: 0.2666573851208479 Gini: 0.5213777408707718 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.11648804145895195:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6947.0 | 371.0 | 0.0507 | (371.0/7318.0) |
| 1 | 1 | 255.0 | 213.0 | 0.5449 | (255.0/468.0) |
| 2 | Total | 7202.0 | 584.0 | 0.0804 | (626.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.116488 | 0.404943 | 183.0 |
| 1 | max f2 | 0.059653 | 0.444364 | 232.0 |
| 2 | max f0point5 | 0.313459 | 0.393340 | 114.0 |
| 3 | max accuracy | 0.636723 | 0.940149 | 4.0 |
| 4 | max precision | 0.636723 | 0.666667 | 4.0 |
| 5 | max recall | 0.018092 | 1.000000 | 385.0 |
| 6 | max specificity | 0.831927 | 0.999727 | 0.0 |
| 7 | max absolute_mcc | 0.116488 | 0.364948 | 183.0 |
| 8 | max min_per_class_accuracy | 0.041616 | 0.685433 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.059653 | 0.718409 | 232.0 |
| 10 | max tns | 0.831927 | 7316.000000 | 0.0 |
| 11 | max fns | 0.831927 | 468.000000 | 0.0 |
| 12 | max fps | 0.002411 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018092 | 468.000000 | 385.0 |
| 14 | max tnr | 0.831927 | 0.999727 | 0.0 |
| 15 | max fnr | 0.831927 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002411 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018092 | 1.000000 | 385.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.412397 | 7.891793 | 7.891793 | 0.474359 | 0.491015 | 0.474359 | 0.491015 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.377248 | 6.825334 | 7.358563 | 0.410256 | 0.393642 | 0.442308 | 0.442328 | 0.068376 | 0.147436 | 582.533421 | 635.856345 | 0.135547 |
| 2 | 3 | 0.030054 | 0.357686 | 6.185459 | 6.967529 | 0.371795 | 0.367612 | 0.418803 | 0.417423 | 0.061966 | 0.209402 | 518.545913 | 596.752867 | 0.190817 |
| 3 | 4 | 0.040072 | 0.335217 | 5.972167 | 6.718688 | 0.358974 | 0.346325 | 0.403846 | 0.399648 | 0.059829 | 0.269231 | 497.216743 | 571.868836 | 0.243814 |
| 4 | 5 | 0.050090 | 0.315840 | 6.398751 | 6.654701 | 0.384615 | 0.325001 | 0.400000 | 0.384719 | 0.064103 | 0.333333 | 539.875082 | 565.470085 | 0.301357 |
| 5 | 6 | 0.100051 | 0.066033 | 3.250368 | 4.954720 | 0.195373 | 0.170013 | 0.297818 | 0.277504 | 0.162393 | 0.495726 | 225.036803 | 395.471951 | 0.420979 |
| 6 | 7 | 0.150013 | 0.052203 | 0.898128 | 3.603680 | 0.053985 | 0.057538 | 0.216610 | 0.204244 | 0.044872 | 0.540598 | -10.187199 | 260.368004 | 0.415564 |
| 7 | 8 | 0.200103 | 0.047475 | 0.895825 | 2.925847 | 0.053846 | 0.049617 | 0.175866 | 0.165538 | 0.044872 | 0.585470 | -10.417488 | 192.584729 | 0.410012 |
| 8 | 9 | 0.300026 | 0.042814 | 0.791208 | 2.214910 | 0.047558 | 0.044916 | 0.133134 | 0.125365 | 0.079060 | 0.664530 | -20.879199 | 121.491007 | 0.387815 |
| 9 | 10 | 0.400077 | 0.039396 | 0.576627 | 1.805208 | 0.034660 | 0.041025 | 0.108507 | 0.104273 | 0.057692 | 0.722222 | -42.337316 | 80.520778 | 0.342747 |
| 10 | 11 | 0.500000 | 0.036602 | 0.662904 | 1.576923 | 0.039846 | 0.037954 | 0.094786 | 0.091020 | 0.066239 | 0.788462 | -33.709599 | 57.692308 | 0.306909 |
| 11 | 12 | 0.600051 | 0.033968 | 0.555270 | 1.406575 | 0.033376 | 0.035258 | 0.084546 | 0.081722 | 0.055556 | 0.844017 | -44.472971 | 40.657472 | 0.259568 |
| 12 | 13 | 0.699974 | 0.031489 | 0.470448 | 1.272940 | 0.028278 | 0.032719 | 0.076514 | 0.074727 | 0.047009 | 0.891026 | -52.955200 | 27.294048 | 0.203269 |
| 13 | 14 | 0.800026 | 0.028916 | 0.405774 | 1.164493 | 0.024390 | 0.030251 | 0.069995 | 0.069165 | 0.040598 | 0.931624 | -59.422556 | 16.449252 | 0.140014 |
| 14 | 15 | 0.899949 | 0.025224 | 0.384912 | 1.077934 | 0.023136 | 0.027195 | 0.064792 | 0.064505 | 0.038462 | 0.970085 | -61.508800 | 7.793428 | 0.074622 |
| 15 | 16 | 1.000000 | 0.001920 | 0.298992 | 1.000000 | 0.017972 | 0.020950 | 0.060108 | 0.060147 | 0.029915 | 1.000000 | -70.100831 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9257861 | 0.038316343 | 0.9461538 | 0.9307692 | 0.74615383 | 0.9230769 | 0.90384614 | 0.93846154 | 0.9230769 | 0.9307692 | 0.90384614 | 0.9653846 | 0.9307692 | 0.9 | 0.90384614 | 0.9461538 | 0.9269231 | 0.9230769 | 0.9150579 | 0.95752895 | 0.93822396 | 0.94208497 | 0.9034749 | 0.9498069 | 0.94208497 | 0.9459459 | 0.95366794 | 0.9150579 | 0.95366794 | 0.93050194 | 0.93822396 | 0.9459459 |
| 1 | auc | 0.7684468 | 0.069762915 | 0.82146513 | 0.6336 | 0.7571166 | 0.78003585 | 0.7739681 | 0.7768095 | 0.8135246 | 0.70176715 | 0.77708334 | 0.7477421 | 0.79994196 | 0.7123464 | 0.68489796 | 0.728 | 0.8965164 | 0.79094076 | 0.61986804 | 0.8710526 | 0.82044446 | 0.84200877 | 0.7660212 | 0.7092369 | 0.7340535 | 0.7511111 | 0.7259475 | 0.74529546 | 0.71322536 | 0.8411968 | 0.7873116 | 0.9308743 |
| 2 | err | 0.07421394 | 0.038316343 | 0.053846154 | 0.06923077 | 0.25384617 | 0.07692308 | 0.09615385 | 0.06153846 | 0.07692308 | 0.06923077 | 0.09615385 | 0.034615386 | 0.06923077 | 0.1 | 0.09615385 | 0.053846154 | 0.073076926 | 0.07692308 | 0.08494209 | 0.042471044 | 0.06177606 | 0.057915058 | 0.096525095 | 0.05019305 | 0.057915058 | 0.054054055 | 0.046332046 | 0.08494209 | 0.046332046 | 0.06949807 | 0.06177606 | 0.054054055 |
| 3 | err_count | 19.266666 | 9.968687 | 14.0 | 18.0 | 66.0 | 20.0 | 25.0 | 16.0 | 20.0 | 18.0 | 25.0 | 9.0 | 18.0 | 26.0 | 25.0 | 14.0 | 19.0 | 20.0 | 22.0 | 11.0 | 16.0 | 15.0 | 25.0 | 13.0 | 15.0 | 14.0 | 12.0 | 22.0 | 12.0 | 18.0 | 16.0 | 14.0 |
| 4 | f0point5 | 0.43347213 | 0.12684888 | 0.5555556 | 0.22727273 | 0.2173913 | 0.52380955 | 0.41984734 | 0.5072464 | 0.39772728 | 0.40983605 | 0.4017857 | 0.6557377 | 0.35714287 | 0.42056075 | 0.3153153 | 0.37878788 | 0.49242425 | 0.3488372 | 0.18987341 | 0.72289157 | 0.32467532 | 0.4 | 0.38135594 | 0.32608697 | 0.45454547 | 0.32786885 | 0.5645161 | 0.5660377 | 0.45454547 | 0.59090906 | 0.5294118 | 0.5421687 |
| 5 | f1 | 0.439329 | 0.11267185 | 0.53333336 | 0.25 | 0.29787233 | 0.52380955 | 0.4680851 | 0.46666667 | 0.4117647 | 0.35714287 | 0.41860464 | 0.64 | 0.35714287 | 0.4090909 | 0.35897437 | 0.41666666 | 0.5777778 | 0.375 | 0.21428572 | 0.6857143 | 0.3846154 | 0.44444445 | 0.41860464 | 0.31578946 | 0.3478261 | 0.36363637 | 0.53846157 | 0.5217391 | 0.4 | 0.59090906 | 0.5294118 | 0.5625 |
| 6 | f2 | 0.45609865 | 0.11224441 | 0.51282054 | 0.2777778 | 0.47297296 | 0.52380955 | 0.52884614 | 0.43209878 | 0.42682928 | 0.3164557 | 0.4368932 | 0.625 | 0.35714287 | 0.39823008 | 0.41666666 | 0.46296296 | 0.6989247 | 0.4054054 | 0.24590164 | 0.65217394 | 0.4716981 | 0.5 | 0.46391752 | 0.30612245 | 0.28169015 | 0.40816328 | 0.5147059 | 0.48387095 | 0.35714287 | 0.59090906 | 0.5294118 | 0.58441556 |
| 7 | lift_top_group | 8.111909 | 3.9474502 | 5.4166665 | 0.0 | 4.814815 | 8.253968 | 9.122807 | 5.098039 | 5.4166665 | 5.098039 | 8.666667 | 13.333333 | 6.1904764 | 7.536232 | 0.0 | 8.666667 | 10.833333 | 6.1904764 | 0.0 | 13.631579 | 9.592592 | 7.848485 | 9.592592 | 8.633333 | 10.791667 | 9.592592 | 6.1666665 | 9.961538 | 14.388889 | 11.772727 | 15.235294 | 11.511111 |
| 8 | logloss | 0.18608738 | 0.03838919 | 0.17519845 | 0.15952156 | 0.23738411 | 0.23524071 | 0.214199 | 0.20057182 | 0.19196038 | 0.2202647 | 0.22763006 | 0.14171809 | 0.17853108 | 0.2662332 | 0.19995767 | 0.1398787 | 0.15252605 | 0.18098983 | 0.17144848 | 0.17547432 | 0.1274267 | 0.13976464 | 0.2100522 | 0.14634334 | 0.20664956 | 0.13804443 | 0.16760817 | 0.27292246 | 0.16771628 | 0.20852976 | 0.18097414 | 0.14786173 |
| 9 | max_per_class_error | 0.5224512 | 0.13457388 | 0.5 | 0.7 | 0.25619835 | 0.47619048 | 0.42105263 | 0.5882353 | 0.5625 | 0.7058824 | 0.55 | 0.3846154 | 0.64285713 | 0.6086956 | 0.53333336 | 0.5 | 0.1875 | 0.5714286 | 0.72727275 | 0.36842105 | 0.44444445 | 0.45454547 | 0.5 | 0.7 | 0.75 | 0.5555556 | 0.5 | 0.53846157 | 0.6666667 | 0.4090909 | 0.47058824 | 0.4 |
| 10 | mcc | 0.4106804 | 0.11399116 | 0.5061643 | 0.21811141 | 0.29110435 | 0.48196852 | 0.42684937 | 0.43903658 | 0.37150094 | 0.3308808 | 0.36753538 | 0.6223984 | 0.3205575 | 0.35503012 | 0.32000926 | 0.39532694 | 0.57020056 | 0.3377009 | 0.17611554 | 0.66593593 | 0.37535542 | 0.42312035 | 0.37339294 | 0.2902457 | 0.35282516 | 0.34259817 | 0.5159169 | 0.48093423 | 0.385307 | 0.5529344 | 0.49635392 | 0.5350011 |
| 11 | mean_per_class_accuracy | 0.7162048 | 0.061586678 | 0.73770493 | 0.628 | 0.7607897 | 0.74098426 | 0.754204 | 0.6935367 | 0.696209 | 0.6347132 | 0.6958333 | 0.7995951 | 0.66027874 | 0.6703357 | 0.69863945 | 0.732 | 0.8734631 | 0.68989545 | 0.60813785 | 0.80745614 | 0.7537778 | 0.752566 | 0.716805 | 0.6379518 | 0.61882716 | 0.7042222 | 0.7397959 | 0.7136018 | 0.6585695 | 0.7764672 | 0.74817693 | 0.7836065 |
| 12 | mean_per_class_error | 0.28379518 | 0.061586678 | 0.26229507 | 0.372 | 0.23921028 | 0.25901574 | 0.24579602 | 0.30646333 | 0.303791 | 0.36528686 | 0.30416667 | 0.20040485 | 0.33972126 | 0.3296643 | 0.30136055 | 0.268 | 0.12653689 | 0.31010452 | 0.39186218 | 0.19254386 | 0.24622223 | 0.24743402 | 0.28319502 | 0.36204818 | 0.38117284 | 0.29577777 | 0.26020408 | 0.28639814 | 0.3414305 | 0.2235328 | 0.25182304 | 0.21639344 |
| 13 | mse | 0.048013505 | 0.0115856435 | 0.04561956 | 0.03734191 | 0.062249042 | 0.06324184 | 0.05674708 | 0.052391905 | 0.05118672 | 0.05663857 | 0.06138596 | 0.03426237 | 0.045859095 | 0.07072838 | 0.051660877 | 0.032212947 | 0.041936602 | 0.04636397 | 0.04025503 | 0.04776346 | 0.03045957 | 0.03497452 | 0.055711087 | 0.03448788 | 0.052411467 | 0.032581482 | 0.04271789 | 0.07445501 | 0.0409827 | 0.057256978 | 0.04657975 | 0.039941534 |
| 14 | null_deviance | 118.05222 | 23.884657 | 120.223526 | 87.37807 | 131.24835 | 147.85797 | 136.7752 | 125.73113 | 120.223526 | 125.73113 | 142.31174 | 103.75808 | 109.23703 | 158.97968 | 114.7255 | 87.37807 | 120.223526 | 109.23703 | 92.701996 | 136.65318 | 81.80913 | 92.701996 | 131.12575 | 87.25086 | 120.09978 | 81.80913 | 109.11213 | 175.61775 | 98.16257 | 153.29364 | 125.607956 | 114.60118 |
| 15 | pr_auc | 0.30712536 | 0.12510924 | 0.38265663 | 0.08604425 | 0.18603659 | 0.43422827 | 0.34908825 | 0.30834338 | 0.250665 | 0.21574108 | 0.32062915 | 0.45086282 | 0.22621042 | 0.32452023 | 0.16171136 | 0.24604824 | 0.39557403 | 0.18817537 | 0.08442405 | 0.5609233 | 0.22187671 | 0.30661696 | 0.3349828 | 0.21925664 | 0.30752084 | 0.15740344 | 0.2801921 | 0.5123243 | 0.29992765 | 0.54053277 | 0.4482267 | 0.41301748 |
| 16 | precision | 0.43479 | 0.14236629 | 0.5714286 | 0.21428572 | 0.18421052 | 0.52380955 | 0.39285713 | 0.53846157 | 0.3888889 | 0.45454547 | 0.39130434 | 0.6666667 | 0.35714287 | 0.42857143 | 0.29166666 | 0.35714287 | 0.44827586 | 0.33333334 | 0.1764706 | 0.75 | 0.29411766 | 0.375 | 0.36 | 0.33333334 | 0.5714286 | 0.30769232 | 0.5833333 | 0.6 | 0.5 | 0.59090906 | 0.5294118 | 0.5294118 |
| 17 | r2 | 0.13703783 | 0.08409441 | 0.21007118 | -0.009725296 | 0.03396804 | 0.14820717 | 0.16224009 | 0.14265487 | 0.11367256 | 0.07316216 | 0.13548109 | 0.27868694 | 0.099862136 | 0.12286956 | 0.049721044 | 0.12896195 | 0.27384368 | 0.089952305 | 0.010136479 | 0.29736394 | 0.091885164 | 0.13998322 | 0.13850728 | 0.070890926 | 0.095726945 | 0.028622989 | 0.16456014 | 0.1755503 | 0.07248294 | 0.26335725 | 0.24049193 | 0.26794586 |
| 18 | recall | 0.47868136 | 0.13701274 | 0.5 | 0.3 | 0.7777778 | 0.52380955 | 0.57894737 | 0.4117647 | 0.4375 | 0.29411766 | 0.45 | 0.61538464 | 0.35714287 | 0.39130434 | 0.46666667 | 0.5 | 0.8125 | 0.42857143 | 0.27272728 | 0.6315789 | 0.5555556 | 0.54545456 | 0.5 | 0.3 | 0.25 | 0.44444445 | 0.5 | 0.46153846 | 0.33333334 | 0.59090906 | 0.5294118 | 0.6 |
| 19 | residual_deviance | 96.60139 | 19.973774 | 91.103195 | 82.95122 | 123.43974 | 122.32517 | 111.383484 | 104.29734 | 99.8194 | 114.53764 | 118.36764 | 73.69341 | 92.83616 | 138.44127 | 103.97798 | 72.73692 | 79.313545 | 94.11471 | 88.81032 | 90.8957 | 66.007034 | 72.39808 | 108.807045 | 75.80585 | 107.04447 | 71.50701 | 86.82104 | 141.37383 | 86.87704 | 108.01841 | 93.744606 | 76.59238 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:12:32 | 0.000 sec | 2 | .86E1 | 15.0 | 0.452125 | 0.452064 | 0.452547 | 0.016677 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:12:32 | 0.003 sec | 4 | .53E1 | 15.0 | 0.450694 | 0.450647 | 0.451181 | 0.01661 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:12:32 | 0.005 sec | 6 | .33E1 | 15.0 | 0.448443 | 0.448417 | 0.449031 | 0.016506 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:12:32 | 0.008 sec | 8 | .2E1 | 15.0 | 0.444945 | 0.444948 | 0.445686 | 0.016346 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:12:32 | 0.011 sec | 10 | .13E1 | 15.0 | 0.439672 | 0.439714 | 0.440628 | 0.01611 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:12:32 | 0.014 sec | 12 | .79E0 | 15.0 | 0.432067 | 0.432153 | 0.433308 | 0.01578 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:12:32 | 0.016 sec | 14 | .49E0 | 15.0 | 0.42192 | 0.422045 | 0.423476 | 0.015364 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:12:32 | 0.019 sec | 16 | .3E0 | 15.0 | 0.410012 | 0.410147 | 0.411826 | 0.014917 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:12:32 | 0.021 sec | 18 | .19E0 | 15.0 | 0.398279 | 0.398384 | 0.400227 | 0.014538 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:12:32 | 0.024 sec | 20 | .12E0 | 15.0 | 0.388709 | 0.388766 | 0.390731 | 0.014294 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:12:32 | 0.027 sec | 22 | .73E-1 | 15.0 | 0.382026 | 0.382045 | 0.384158 | 0.014174 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:12:32 | 0.029 sec | 24 | .45E-1 | 15.0 | 0.377801 | 0.37779 | 0.380131 | 0.01413 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:12:32 | 0.032 sec | 26 | .28E-1 | 15.0 | 0.375267 | 0.375215 | 0.377868 | 0.014117 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:12:32 | 0.034 sec | 28 | .17E-1 | 15.0 | 0.373769 | 0.373649 | 0.376668 | 0.014108 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:12:32 | 0.295 sec | 29 | None | NaN | 29.0 | 0.219803 | 0.186445 | 0.144819 | 0.774173 | 0.283334 | 8.318376 | 0.077704 | 0.218554 | 0.186337 | 0.154308 | 0.76566 | 0.29868 | 9.152564 | 0.062147 | ||||||
| 15 | 2021-07-15 20:12:32 | 0.037 sec | 30 | .11E-1 | 15.0 | 0.372891 | 0.372675 | 0.376082 | 0.014092 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:12:32 | 0.039 sec | 32 | .67E-2 | 15.0 | 0.372382 | 0.372054 | 0.376502 | 0.014232 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:12:32 | 0.042 sec | 34 | .42E-2 | 15.0 | 0.372097 | 0.371657 | 0.378844 | 0.014553 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:12:32 | 0.044 sec | 35 | .26E-2 | 15.0 | 0.371945 | 0.371402 | 0.382704 | 0.014294 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.551740 | 1.000000 | 0.283018 |
| 1 | Merchant_ID | 0.203552 | 0.368926 | 0.104413 |
| 2 | Average_Transaction_Frequency | 0.202469 | 0.366965 | 0.103858 |
| 3 | Minimum_Transaction_Amount | 0.188421 | 0.341502 | 0.096651 |
| 4 | Channel_ID | 0.169383 | 0.306997 | 0.086886 |
| 5 | Card_Type.1 | 0.145256 | 0.263268 | 0.074509 |
| 6 | Card_Type.0 | 0.143886 | 0.260785 | 0.073807 |
| 7 | Transaction_Amount | 0.107339 | 0.194546 | 0.055060 |
| 8 | Transaction_Date | 0.083178 | 0.150756 | 0.042667 |
| 9 | Month | 0.059291 | 0.107461 | 0.030413 |
| 10 | Average_Transaction_Amount | 0.052848 | 0.095784 | 0.027109 |
| 11 | Maximum_Transaction_Amount | 0.019714 | 0.035731 | 0.010112 |
| 12 | Day | 0.017947 | 0.032528 | 0.009206 |
| 13 | City_ID | 0.004468 | 0.008098 | 0.002292 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201235 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.00425 ) | nlambda = 30, lambda.max = 8.6835, lambda.min = 0.00425, lambda.1s... | 14 | 14 | 34 | automl_training_py_250_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.048019023708569204 RMSE: 0.21913243417752928 LogLoss: 0.18613773452177765 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938275 Residual deviance: 2898.5368019731222 AIC: 2928.5368019731222 AUC: 0.7701607440265544 AUCPR: 0.2895046536960743 Gini: 0.5403214880531089 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2330585150209259:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7009.0 | 309.0 | 0.0422 | (309.0/7318.0) |
| 1 | 1 | 268.0 | 200.0 | 0.5726 | (268.0/468.0) |
| 2 | Total | 7277.0 | 509.0 | 0.0741 | (577.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.233059 | 0.409417 | 163.0 |
| 1 | max f2 | 0.063635 | 0.442660 | 232.0 |
| 2 | max f0point5 | 0.364860 | 0.429878 | 97.0 |
| 3 | max accuracy | 0.509836 | 0.940534 | 24.0 |
| 4 | max precision | 0.622520 | 0.666667 | 7.0 |
| 5 | max recall | 0.017481 | 1.000000 | 379.0 |
| 6 | max specificity | 0.816224 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.271950 | 0.370802 | 151.0 |
| 8 | max min_per_class_accuracy | 0.041511 | 0.685897 | 287.0 |
| 9 | max mean_per_class_accuracy | 0.048398 | 0.714472 | 262.0 |
| 10 | max tns | 0.816224 | 7317.000000 | 0.0 |
| 11 | max fns | 0.816224 | 468.000000 | 0.0 |
| 12 | max fps | 0.000704 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017481 | 468.000000 | 379.0 |
| 14 | max tnr | 0.816224 | 0.999863 | 0.0 |
| 15 | max fnr | 0.816224 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000704 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017481 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.450244 | 8.744959 | 8.744959 | 0.525641 | 0.522335 | 0.525641 | 0.522335 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.409379 | 7.038626 | 7.891793 | 0.423077 | 0.427923 | 0.474359 | 0.475129 | 0.070513 | 0.158120 | 603.862590 | 689.179268 | 0.146914 |
| 2 | 3 | 0.030054 | 0.382777 | 8.318376 | 8.033987 | 0.500000 | 0.395735 | 0.482906 | 0.448665 | 0.083333 | 0.241453 | 731.837607 | 703.398714 | 0.224918 |
| 3 | 4 | 0.040072 | 0.358641 | 6.825334 | 7.731824 | 0.410256 | 0.370268 | 0.464744 | 0.429065 | 0.068376 | 0.309829 | 582.533421 | 673.182391 | 0.287009 |
| 4 | 5 | 0.050090 | 0.329008 | 5.972167 | 7.379893 | 0.358974 | 0.344389 | 0.443590 | 0.412130 | 0.059829 | 0.369658 | 497.216743 | 637.989261 | 0.340005 |
| 5 | 6 | 0.100051 | 0.064539 | 2.608848 | 4.997433 | 0.156812 | 0.156585 | 0.300385 | 0.284522 | 0.130342 | 0.500000 | 160.884802 | 399.743261 | 0.425526 |
| 6 | 7 | 0.150013 | 0.052151 | 0.940896 | 3.646411 | 0.056555 | 0.056971 | 0.219178 | 0.208736 | 0.047009 | 0.547009 | -5.910399 | 264.641143 | 0.422384 |
| 7 | 8 | 0.200103 | 0.047533 | 1.023800 | 2.989917 | 0.061538 | 0.049661 | 0.179718 | 0.168916 | 0.051282 | 0.598291 | 2.380013 | 198.991694 | 0.423653 |
| 8 | 9 | 0.300026 | 0.042611 | 0.705672 | 2.229154 | 0.042416 | 0.044826 | 0.133990 | 0.127588 | 0.070513 | 0.668803 | -29.432799 | 122.915386 | 0.392362 |
| 9 | 10 | 0.400077 | 0.039050 | 0.619340 | 1.826571 | 0.037227 | 0.040761 | 0.109791 | 0.105875 | 0.061966 | 0.730769 | -38.066006 | 82.657118 | 0.351841 |
| 10 | 11 | 0.500000 | 0.036288 | 0.684288 | 1.598291 | 0.041131 | 0.037588 | 0.096070 | 0.092228 | 0.068376 | 0.799145 | -31.571199 | 59.829060 | 0.318276 |
| 11 | 12 | 0.600051 | 0.033648 | 0.619340 | 1.435062 | 0.037227 | 0.034971 | 0.086259 | 0.082681 | 0.061966 | 0.861111 | -38.066006 | 43.506231 | 0.277755 |
| 12 | 13 | 0.699974 | 0.030844 | 0.384912 | 1.285151 | 0.023136 | 0.032333 | 0.077248 | 0.075494 | 0.038462 | 0.899573 | -61.508800 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.027767 | 0.405774 | 1.175176 | 0.024390 | 0.029401 | 0.070637 | 0.069729 | 0.040598 | 0.940171 | -59.422556 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.023173 | 0.342144 | 1.082683 | 0.020566 | 0.025570 | 0.065078 | 0.064826 | 0.034188 | 0.974359 | -65.785600 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.000504 | 0.256279 | 1.000000 | 0.015404 | 0.017668 | 0.060108 | 0.060108 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04850857131461125 RMSE: 0.22024661476311333 LogLoss: 0.1858556367763951 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311445 Residual deviance: 723.7218496072825 AIC: 753.7218496072825 AUC: 0.785647564336089 AUCPR: 0.29061223224647204 Gini: 0.5712951286721779 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.15400582231559215:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1740.0 | 90.0 | 0.0492 | (90.0/1830.0) |
| 1 | 1 | 63.0 | 54.0 | 0.5385 | (63.0/117.0) |
| 2 | Total | 1803.0 | 144.0 | 0.0786 | (153.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.154006 | 0.413793 | 121.0 |
| 1 | max f2 | 0.080432 | 0.453834 | 142.0 |
| 2 | max f0point5 | 0.154006 | 0.389610 | 121.0 |
| 3 | max accuracy | 0.477461 | 0.941962 | 9.0 |
| 4 | max precision | 0.655375 | 1.000000 | 0.0 |
| 5 | max recall | 0.021330 | 1.000000 | 361.0 |
| 6 | max specificity | 0.655375 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.154006 | 0.374467 | 121.0 |
| 8 | max min_per_class_accuracy | 0.042337 | 0.709402 | 234.0 |
| 9 | max mean_per_class_accuracy | 0.043420 | 0.724744 | 229.0 |
| 10 | max tns | 0.655375 | 1830.000000 | 0.0 |
| 11 | max fns | 0.655375 | 116.000000 | 0.0 |
| 12 | max fps | 0.000571 | 1830.000000 | 399.0 |
| 13 | max tps | 0.021330 | 117.000000 | 361.0 |
| 14 | max tnr | 0.655375 | 1.000000 | 0.0 |
| 15 | max fnr | 0.655375 | 0.991453 | 0.0 |
| 16 | max fpr | 0.000571 | 1.000000 | 399.0 |
| 17 | max tpr | 0.021330 | 1.000000 | 361.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.21 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.450507 | 7.488462 | 7.488462 | 0.450000 | 0.500826 | 0.450000 | 0.500826 | 0.076923 | 0.076923 | 648.846154 | 648.846154 | 0.070912 |
| 1 | 2 | 0.020031 | 0.416130 | 8.758435 | 8.107166 | 0.526316 | 0.433119 | 0.487179 | 0.467841 | 0.085470 | 0.162393 | 775.843455 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.392696 | 4.160256 | 6.769231 | 0.250000 | 0.405336 | 0.406780 | 0.446653 | 0.042735 | 0.205128 | 316.025641 | 576.923077 | 0.186003 |
| 3 | 4 | 0.040062 | 0.370132 | 6.130904 | 6.613741 | 0.368421 | 0.378597 | 0.397436 | 0.430075 | 0.059829 | 0.264957 | 513.090418 | 561.374096 | 0.239274 |
| 4 | 5 | 0.050334 | 0.355609 | 4.992308 | 6.282836 | 0.300000 | 0.363479 | 0.377551 | 0.416484 | 0.051282 | 0.316239 | 399.230769 | 528.283621 | 0.282906 |
| 5 | 6 | 0.100154 | 0.067546 | 3.774253 | 5.034977 | 0.226804 | 0.185692 | 0.302564 | 0.301680 | 0.188034 | 0.504274 | 277.425324 | 403.497699 | 0.429957 |
| 6 | 7 | 0.149974 | 0.051880 | 0.857785 | 3.647348 | 0.051546 | 0.058066 | 0.219178 | 0.220753 | 0.042735 | 0.547009 | -14.221517 | 264.734809 | 0.422418 |
| 7 | 8 | 0.200308 | 0.047538 | 1.018838 | 2.986851 | 0.061224 | 0.049431 | 0.179487 | 0.177703 | 0.051282 | 0.598291 | 1.883830 | 198.685076 | 0.423427 |
| 8 | 9 | 0.299949 | 0.042276 | 1.029342 | 2.336582 | 0.061856 | 0.044914 | 0.140411 | 0.133592 | 0.102564 | 0.700855 | 2.934179 | 133.658237 | 0.426538 |
| 9 | 10 | 0.400103 | 0.039144 | 0.512032 | 1.879859 | 0.030769 | 0.040661 | 0.112965 | 0.110329 | 0.051282 | 0.752137 | -48.796844 | 87.985912 | 0.374541 |
| 10 | 11 | 0.500257 | 0.036459 | 0.512032 | 1.606013 | 0.030769 | 0.037865 | 0.096509 | 0.095822 | 0.051282 | 0.803419 | -48.796844 | 60.601274 | 0.322544 |
| 11 | 12 | 0.599897 | 0.033773 | 0.943563 | 1.495983 | 0.056701 | 0.035158 | 0.089897 | 0.085746 | 0.094017 | 0.897436 | -5.643669 | 49.598261 | 0.316562 |
| 12 | 13 | 0.700051 | 0.031145 | 0.426693 | 1.343003 | 0.025641 | 0.032475 | 0.080704 | 0.078124 | 0.042735 | 0.940171 | -57.330703 | 34.300280 | 0.255471 |
| 13 | 14 | 0.799692 | 0.028175 | 0.085778 | 1.186354 | 0.005155 | 0.029702 | 0.071291 | 0.072091 | 0.008547 | 0.948718 | -91.422152 | 18.635443 | 0.158554 |
| 14 | 15 | 0.899846 | 0.023951 | 0.341354 | 1.092305 | 0.020513 | 0.026133 | 0.065639 | 0.066976 | 0.034188 | 0.982906 | -65.864563 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.000490 | 0.170677 | 1.000000 | 0.010256 | 0.018576 | 0.060092 | 0.062128 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04832043952416805 RMSE: 0.21981910636741303 LogLoss: 0.18754319785010057 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.2097763014326 Residual deviance: 2920.422676921766 AIC: 2950.422676921766 AUC: 0.7617369534901647 AUCPR: 0.27987968924420253 Gini: 0.5234739069803294 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.26022535438412825:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7028.0 | 290.0 | 0.0396 | (290.0/7318.0) |
| 1 | 1 | 275.0 | 193.0 | 0.5876 | (275.0/468.0) |
| 2 | Total | 7303.0 | 483.0 | 0.0726 | (565.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.260225 | 0.405889 | 154.0 |
| 1 | max f2 | 0.061489 | 0.436641 | 234.0 |
| 2 | max f0point5 | 0.358380 | 0.426471 | 102.0 |
| 3 | max accuracy | 0.447696 | 0.940277 | 47.0 |
| 4 | max precision | 0.748060 | 0.600000 | 3.0 |
| 5 | max recall | 0.014241 | 1.000000 | 388.0 |
| 6 | max specificity | 0.841183 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.260225 | 0.367308 | 154.0 |
| 8 | max min_per_class_accuracy | 0.042441 | 0.685897 | 282.0 |
| 9 | max mean_per_class_accuracy | 0.061489 | 0.711167 | 234.0 |
| 10 | max tns | 0.841183 | 7317.000000 | 0.0 |
| 11 | max fns | 0.841183 | 468.000000 | 0.0 |
| 12 | max fps | 0.000732 | 7318.000000 | 399.0 |
| 13 | max tps | 0.014241 | 468.000000 | 388.0 |
| 14 | max tnr | 0.841183 | 0.999863 | 0.0 |
| 15 | max fnr | 0.841183 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000732 | 1.000000 | 399.0 |
| 17 | max tpr | 0.014241 | 1.000000 | 388.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.05 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.446160 | 8.531668 | 8.531668 | 0.512821 | 0.527370 | 0.512821 | 0.527370 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.407657 | 7.251918 | 7.891793 | 0.435897 | 0.424934 | 0.474359 | 0.476152 | 0.072650 | 0.158120 | 625.191760 | 689.179268 | 0.146914 |
| 2 | 3 | 0.030054 | 0.379241 | 7.465209 | 7.749598 | 0.448718 | 0.392902 | 0.465812 | 0.448402 | 0.074786 | 0.232906 | 646.520929 | 674.959822 | 0.215825 |
| 3 | 4 | 0.040072 | 0.356510 | 7.678501 | 7.731824 | 0.461538 | 0.367915 | 0.464744 | 0.428280 | 0.076923 | 0.309829 | 667.850099 | 673.182391 | 0.287009 |
| 4 | 5 | 0.050090 | 0.327270 | 5.545584 | 7.294576 | 0.333333 | 0.342202 | 0.438462 | 0.411065 | 0.055556 | 0.365385 | 454.558405 | 629.457594 | 0.335458 |
| 5 | 6 | 0.100051 | 0.063089 | 2.523312 | 4.912006 | 0.151671 | 0.147799 | 0.295250 | 0.279601 | 0.126068 | 0.491453 | 152.331202 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.057580 | 1.026432 | 3.617924 | 0.061697 | 0.060243 | 0.217466 | 0.206544 | 0.051282 | 0.542735 | 2.643201 | 261.792384 | 0.417838 |
| 7 | 8 | 0.200103 | 0.050042 | 0.639875 | 2.872456 | 0.038462 | 0.053226 | 0.172657 | 0.168165 | 0.032051 | 0.574786 | -36.012492 | 187.245592 | 0.398645 |
| 8 | 9 | 0.300026 | 0.043725 | 0.812592 | 2.186422 | 0.048843 | 0.046360 | 0.131421 | 0.127598 | 0.081197 | 0.655983 | -18.740799 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.039867 | 0.533914 | 1.773163 | 0.032092 | 0.041697 | 0.106581 | 0.106116 | 0.053419 | 0.709402 | -46.608626 | 77.316267 | 0.329107 |
| 10 | 11 | 0.500000 | 0.036808 | 0.833976 | 1.585470 | 0.050129 | 0.038233 | 0.095299 | 0.092550 | 0.083333 | 0.792735 | -16.602399 | 58.547009 | 0.311456 |
| 11 | 12 | 0.600051 | 0.034127 | 0.555270 | 1.413697 | 0.033376 | 0.035454 | 0.084974 | 0.083030 | 0.055556 | 0.848291 | -44.472971 | 41.369662 | 0.264115 |
| 12 | 13 | 0.699974 | 0.031337 | 0.513216 | 1.285151 | 0.030848 | 0.032733 | 0.077248 | 0.075850 | 0.051282 | 0.899573 | -48.678400 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.028065 | 0.341705 | 1.167163 | 0.020539 | 0.029783 | 0.070156 | 0.070089 | 0.034188 | 0.933761 | -65.829521 | 16.716338 | 0.142288 |
| 14 | 15 | 0.899949 | 0.023444 | 0.406296 | 1.082683 | 0.024422 | 0.025847 | 0.065078 | 0.065176 | 0.040598 | 0.974359 | -59.370400 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.000511 | 0.256279 | 1.000000 | 0.015404 | 0.018001 | 0.060108 | 0.060456 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9273136 | 0.04776632 | 0.9692308 | 0.9423077 | 0.9307692 | 0.9076923 | 0.9461538 | 0.95384616 | 0.96153843 | 0.9153846 | 0.93846154 | 0.9346154 | 0.9230769 | 0.9653846 | 0.93846154 | 0.93846154 | 0.9076923 | 0.7 | 0.93822396 | 0.95752895 | 0.93436295 | 0.93436295 | 0.9266409 | 0.9227799 | 0.8918919 | 0.94208497 | 0.9266409 | 0.96525097 | 0.87644786 | 0.9459459 | 0.9459459 | 0.93822396 |
| 1 | auc | 0.77366304 | 0.07092094 | 0.85215056 | 0.81283087 | 0.772 | 0.730625 | 0.6721835 | 0.8598402 | 0.7229932 | 0.83145833 | 0.6557734 | 0.735482 | 0.6509872 | 0.82949984 | 0.72761905 | 0.7520325 | 0.78240234 | 0.6702041 | 0.7707943 | 0.7827869 | 0.92489713 | 0.7594298 | 0.81988335 | 0.7246899 | 0.6872385 | 0.89407897 | 0.79068696 | 0.847166 | 0.7177929 | 0.7845481 | 0.79120076 | 0.85661596 |
| 2 | err | 0.07268637 | 0.04776632 | 0.03076923 | 0.057692308 | 0.06923077 | 0.092307694 | 0.053846154 | 0.046153847 | 0.03846154 | 0.08461539 | 0.06153846 | 0.06538462 | 0.07692308 | 0.034615386 | 0.06153846 | 0.06153846 | 0.092307694 | 0.3 | 0.06177606 | 0.042471044 | 0.06563707 | 0.06563707 | 0.07335907 | 0.077220075 | 0.10810811 | 0.057915058 | 0.07335907 | 0.034749035 | 0.12355212 | 0.054054055 | 0.054054055 | 0.06177606 |
| 3 | err_count | 18.866667 | 12.417267 | 8.0 | 15.0 | 18.0 | 24.0 | 14.0 | 12.0 | 10.0 | 22.0 | 16.0 | 17.0 | 20.0 | 9.0 | 16.0 | 16.0 | 24.0 | 78.0 | 16.0 | 11.0 | 17.0 | 17.0 | 19.0 | 20.0 | 28.0 | 15.0 | 19.0 | 9.0 | 32.0 | 14.0 | 14.0 | 16.0 |
| 4 | f0point5 | 0.4697138 | 0.13020281 | 0.6730769 | 0.40983605 | 0.30487806 | 0.41666666 | 0.46296296 | 0.6451613 | 0.71428573 | 0.46296296 | 0.4716981 | 0.45918366 | 0.32051283 | 0.5813953 | 0.42372882 | 0.42857143 | 0.29411766 | 0.14409222 | 0.3846154 | 0.63829786 | 0.5092593 | 0.5084746 | 0.47619048 | 0.47945204 | 0.37878788 | 0.6086956 | 0.42857143 | 0.625 | 0.2631579 | 0.46296296 | 0.5660377 | 0.5487805 |
| 5 | f1 | 0.45367545 | 0.1059443 | 0.6363636 | 0.4 | 0.35714287 | 0.42857143 | 0.41666666 | 0.6666667 | 0.5 | 0.47619048 | 0.3846154 | 0.51428574 | 0.33333334 | 0.5263158 | 0.3846154 | 0.42857143 | 0.36842105 | 0.20408164 | 0.3846154 | 0.5217391 | 0.5641026 | 0.41379312 | 0.51282054 | 0.4117647 | 0.41666666 | 0.6511628 | 0.38709676 | 0.6086956 | 0.3043478 | 0.41666666 | 0.46153846 | 0.5294118 |
| 6 | f2 | 0.45476058 | 0.10865555 | 0.6034483 | 0.390625 | 0.43103448 | 0.44117647 | 0.37878788 | 0.6896552 | 0.3846154 | 0.49019608 | 0.32467532 | 0.58441556 | 0.3472222 | 0.48076922 | 0.35211268 | 0.42857143 | 0.49295774 | 0.34965035 | 0.3846154 | 0.44117647 | 0.6321839 | 0.3488372 | 0.5555556 | 0.36082473 | 0.46296296 | 0.7 | 0.3529412 | 0.59322035 | 0.36082473 | 0.37878788 | 0.38961038 | 0.5113636 |
| 7 | lift_top_group | 8.245462 | 5.22645 | 14.444445 | 6.6666665 | 0.0 | 4.3333335 | 6.1904764 | 15.294118 | 17.333334 | 8.666667 | 15.294118 | 12.380953 | 0.0 | 15.757576 | 0.0 | 6.1904764 | 7.878788 | 5.7777777 | 13.282051 | 17.266666 | 0.0 | 4.5438595 | 10.156863 | 4.111111 | 8.633333 | 9.087719 | 4.796296 | 7.1944447 | 5.0784316 | 12.333333 | 5.0784316 | 9.592592 |
| 8 | logloss | 0.18627481 | 0.032625645 | 0.13070942 | 0.17319196 | 0.14284146 | 0.22976966 | 0.18556349 | 0.15226416 | 0.18874323 | 0.20988274 | 0.21691437 | 0.15624857 | 0.19149312 | 0.13684073 | 0.19523957 | 0.17272513 | 0.15247521 | 0.2155002 | 0.1712358 | 0.17065859 | 0.166535 | 0.22661994 | 0.18330856 | 0.2537105 | 0.2396363 | 0.17204331 | 0.22204176 | 0.13884278 | 0.2232444 | 0.17371088 | 0.20556808 | 0.19068545 |
| 9 | max_per_class_error | 0.52957547 | 0.1339346 | 0.41666666 | 0.61538464 | 0.5 | 0.55 | 0.64285713 | 0.29411766 | 0.6666667 | 0.5 | 0.7058824 | 0.35714287 | 0.64285713 | 0.54545456 | 0.6666667 | 0.5714286 | 0.36363637 | 0.33333334 | 0.61538464 | 0.6 | 0.3125 | 0.68421054 | 0.4117647 | 0.6666667 | 0.5 | 0.2631579 | 0.6666667 | 0.41666666 | 0.5882353 | 0.64285713 | 0.64705884 | 0.5 |
| 10 | mcc | 0.43197608 | 0.112270094 | 0.62325025 | 0.37007472 | 0.33939394 | 0.37899473 | 0.39532694 | 0.6431015 | 0.5659165 | 0.43085715 | 0.37547213 | 0.4921137 | 0.29344437 | 0.5157763 | 0.35774302 | 0.3960511 | 0.3668969 | 0.1844102 | 0.35209507 | 0.52896476 | 0.5400621 | 0.40479234 | 0.4784671 | 0.38519743 | 0.3650876 | 0.62508243 | 0.3543969 | 0.5911619 | 0.25198743 | 0.39521667 | 0.4604842 | 0.49746263 |
| 11 | mean_per_class_accuracy | 0.7135527 | 0.061276406 | 0.7856183 | 0.67813766 | 0.724 | 0.6979167 | 0.6684088 | 0.8385379 | 0.6666667 | 0.725 | 0.6388284 | 0.7970383 | 0.6562137 | 0.7212486 | 0.65442175 | 0.6980255 | 0.77802116 | 0.68435377 | 0.6760475 | 0.69590163 | 0.81905866 | 0.6495614 | 0.76932424 | 0.6540616 | 0.7123431 | 0.8475877 | 0.65214384 | 0.7835695 | 0.6604278 | 0.6683673 | 0.67027223 | 0.73547715 |
| 12 | mean_per_class_error | 0.2864473 | 0.061276406 | 0.21438172 | 0.32186234 | 0.276 | 0.30208334 | 0.33159116 | 0.16146211 | 0.33333334 | 0.275 | 0.36117163 | 0.20296167 | 0.3437863 | 0.27875137 | 0.34557822 | 0.30197445 | 0.22197883 | 0.31564626 | 0.32395247 | 0.30409837 | 0.18094136 | 0.3504386 | 0.23067574 | 0.34593838 | 0.2876569 | 0.15241228 | 0.34785616 | 0.2164305 | 0.3395722 | 0.33163264 | 0.32972777 | 0.26452282 |
| 13 | mse | 0.048090428 | 0.00941489 | 0.03228469 | 0.04434512 | 0.034700196 | 0.061802186 | 0.046563428 | 0.039941516 | 0.047171313 | 0.057626 | 0.054110717 | 0.038827755 | 0.048194554 | 0.03319131 | 0.050128 | 0.043784432 | 0.038011946 | 0.053635135 | 0.042436276 | 0.042398565 | 0.046422448 | 0.058845077 | 0.049262878 | 0.06797391 | 0.06306233 | 0.047568005 | 0.05935026 | 0.03425917 | 0.05806549 | 0.043953806 | 0.053823646 | 0.05097268 |
| 14 | null_deviance | 118.006996 | 16.416685 | 98.28862 | 103.75808 | 87.37807 | 142.31174 | 109.23703 | 125.73113 | 114.7255 | 142.31174 | 125.73113 | 109.23703 | 109.23703 | 92.82863 | 114.7255 | 109.23703 | 92.82863 | 114.7255 | 103.63261 | 114.60118 | 120.09978 | 136.65318 | 125.607956 | 147.73709 | 142.19029 | 136.65318 | 131.12575 | 98.16257 | 125.607956 | 109.11213 | 125.607956 | 131.12575 |
| 15 | pr_auc | 0.3161848 | 0.12039268 | 0.4361766 | 0.22848935 | 0.15249723 | 0.26688632 | 0.20492762 | 0.5876478 | 0.4184242 | 0.41155854 | 0.35431445 | 0.4239654 | 0.12516046 | 0.4334231 | 0.20340407 | 0.29239625 | 0.21515457 | 0.111751005 | 0.23779371 | 0.45081416 | 0.35641783 | 0.31898832 | 0.38178745 | 0.26301426 | 0.3407557 | 0.56156284 | 0.25154418 | 0.36087647 | 0.16179027 | 0.2681306 | 0.26935196 | 0.39653853 |
| 16 | precision | 0.49296507 | 0.17384508 | 0.7 | 0.41666666 | 0.2777778 | 0.4090909 | 0.5 | 0.6315789 | 1.0 | 0.45454547 | 0.5555556 | 0.42857143 | 0.3125 | 0.625 | 0.45454547 | 0.42857143 | 0.25925925 | 0.12048193 | 0.3846154 | 0.75 | 0.47826087 | 0.6 | 0.45454547 | 0.53846157 | 0.35714287 | 0.5833333 | 0.46153846 | 0.6363636 | 0.2413793 | 0.5 | 0.6666667 | 0.5625 |
| 17 | r2 | 0.14530149 | 0.080322616 | 0.2666516 | 0.066418506 | 0.06170666 | 0.12961923 | 0.08603726 | 0.346394 | 0.13230458 | 0.18843381 | 0.114528105 | 0.2378757 | 0.05402098 | 0.18082058 | 0.07791758 | 0.14058433 | 0.06184466 | 0.013405378 | 0.10985995 | 0.22291307 | 0.19905755 | 0.13434502 | 0.19674207 | 0.08768353 | 0.11500325 | 0.30023918 | 0.08223267 | 0.2246493 | 0.053210728 | 0.1403891 | 0.12237628 | 0.21178 |
| 18 | recall | 0.47042453 | 0.1339346 | 0.5833333 | 0.3846154 | 0.5 | 0.45 | 0.35714287 | 0.7058824 | 0.33333334 | 0.5 | 0.29411766 | 0.64285713 | 0.35714287 | 0.45454547 | 0.33333334 | 0.42857143 | 0.6363636 | 0.6666667 | 0.3846154 | 0.4 | 0.6875 | 0.31578946 | 0.5882353 | 0.33333334 | 0.5 | 0.7368421 | 0.33333334 | 0.5833333 | 0.4117647 | 0.35714287 | 0.3529412 | 0.5 |
| 19 | residual_deviance | 96.68038 | 16.882318 | 67.9689 | 90.05982 | 74.27756 | 119.480225 | 96.49301 | 79.17736 | 98.14648 | 109.13902 | 112.79547 | 81.24925 | 99.57642 | 71.15718 | 101.524574 | 89.81706 | 79.28711 | 112.060104 | 88.70015 | 88.401146 | 86.26514 | 117.38914 | 94.953835 | 131.42204 | 124.13161 | 89.11843 | 115.01763 | 71.920555 | 115.6406 | 89.98224 | 106.48426 | 98.77506 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:12:45 | 0.000 sec | 2 | .87E1 | 15.0 | 0.452147 | 0.452055 | 0.452462 | 0.011553 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:12:45 | 0.003 sec | 4 | .54E1 | 15.0 | 0.450727 | 0.450631 | 0.451105 | 0.011519 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:12:45 | 0.005 sec | 6 | .33E1 | 15.0 | 0.448495 | 0.448393 | 0.448969 | 0.011466 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:12:45 | 0.008 sec | 8 | .21E1 | 15.0 | 0.445023 | 0.444912 | 0.445641 | 0.011388 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:12:45 | 0.016 sec | 10 | .13E1 | 15.0 | 0.439783 | 0.439662 | 0.440608 | 0.011279 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:12:45 | 0.019 sec | 12 | .8E0 | 15.0 | 0.432216 | 0.432086 | 0.433311 | 0.011141 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:12:45 | 0.022 sec | 14 | .5E0 | 15.0 | 0.4221 | 0.421969 | 0.423497 | 0.011002 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:12:45 | 0.024 sec | 16 | .31E0 | 15.0 | 0.410213 | 0.410095 | 0.411851 | 0.010922 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:12:45 | 0.027 sec | 18 | .19E0 | 15.0 | 0.398492 | 0.3984 | 0.40025 | 0.010963 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:12:45 | 0.030 sec | 20 | .12E0 | 15.0 | 0.388953 | 0.388876 | 0.390759 | 0.011132 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:12:45 | 0.032 sec | 22 | .74E-1 | 15.0 | 0.382296 | 0.382199 | 0.384185 | 0.011366 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:12:45 | 0.035 sec | 24 | .46E-1 | 15.0 | 0.378083 | 0.377934 | 0.380135 | 0.011591 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:12:45 | 0.037 sec | 26 | .29E-1 | 15.0 | 0.375535 | 0.375313 | 0.377816 | 0.011767 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:12:45 | 0.040 sec | 28 | .18E-1 | 15.0 | 0.374009 | 0.373707 | 0.376554 | 0.011886 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:12:45 | 0.043 sec | 30 | .11E-1 | 15.0 | 0.373102 | 0.372713 | 0.375678 | 0.01191 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:12:45 | 0.046 sec | 32 | .68E-2 | 15.0 | 0.372574 | 0.372096 | 0.375296 | 0.01193 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:12:45 | 0.451 sec | 33 | None | NaN | 33.0 | 0.219132 | 0.186138 | 0.150029 | 0.770161 | 0.289505 | 8.744959 | 0.074107 | 0.220247 | 0.185856 | 0.141158 | 0.785648 | 0.290612 | 7.488462 | 0.078582 | ||||||
| 17 | 2021-07-15 20:12:45 | 0.051 sec | 34 | .42E-2 | 15.0 | 0.372275 | 0.371711 | 0.375121 | 0.011934 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:12:45 | 0.054 sec | 36 | .26E-2 | 15.0 | 0.372112 | 0.371473 | 0.377828 | 0.012237 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:12:45 | 0.060 sec | 37 | .16E-2 | 15.0 | 0.372021 | 0.371331 | 0.388513 | 0.013635 | 0.0 | NaN |
See the whole table with table.as_data_frame() Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.575602 | 1.000000 | 0.261576 |
| 1 | Average_Transaction_Frequency | 0.331347 | 0.575653 | 0.150577 |
| 2 | Merchant_ID | 0.208163 | 0.361644 | 0.094597 |
| 3 | Channel_ID | 0.173558 | 0.301524 | 0.078872 |
| 4 | Card_Type.1 | 0.160445 | 0.278743 | 0.072912 |
| 5 | Card_Type.0 | 0.156466 | 0.271829 | 0.071104 |
| 6 | Minimum_Transaction_Amount | 0.148746 | 0.258418 | 0.067596 |
| 7 | Transaction_Amount | 0.125963 | 0.218837 | 0.057242 |
| 8 | Transaction_Date | 0.096485 | 0.167625 | 0.043847 |
| 9 | Maximum_Transaction_Amount | 0.088881 | 0.154415 | 0.040391 |
| 10 | Day | 0.077532 | 0.134698 | 0.035234 |
| 11 | Average_Transaction_Amount | 0.037747 | 0.065578 | 0.017154 |
| 12 | Month | 0.017320 | 0.030089 | 0.007871 |
| 13 | City_ID | 0.002261 | 0.003928 | 0.001027 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201249 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01095 ) | nlambda = 30, lambda.max = 8.6268, lambda.min = 0.01095, lambda.1s... | 14 | 14 | 30 | automl_training_py_285_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.047988591383987325 RMSE: 0.21906298497004767 LogLoss: 0.18521543218372566 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938293 Residual deviance: 2884.1747099649765 AIC: 2914.1747099649765 AUC: 0.7778204077056222 AUCPR: 0.2941196181535852 Gini: 0.5556408154112444 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.12273005104748826:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6953.0 | 365.0 | 0.0499 | (365.0/7318.0) |
| 1 | 1 | 250.0 | 218.0 | 0.5342 | (250.0/468.0) |
| 2 | Total | 7203.0 | 583.0 | 0.079 | (615.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.122730 | 0.414843 | 192.0 |
| 1 | max f2 | 0.071608 | 0.453846 | 223.0 |
| 2 | max f0point5 | 0.334182 | 0.417588 | 107.0 |
| 3 | max accuracy | 0.435245 | 0.940663 | 42.0 |
| 4 | max precision | 0.835207 | 1.000000 | 0.0 |
| 5 | max recall | 0.019257 | 1.000000 | 382.0 |
| 6 | max specificity | 0.835207 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.122730 | 0.375624 | 192.0 |
| 8 | max min_per_class_accuracy | 0.042098 | 0.698718 | 289.0 |
| 9 | max mean_per_class_accuracy | 0.071608 | 0.718521 | 223.0 |
| 10 | max tns | 0.835207 | 7318.000000 | 0.0 |
| 11 | max fns | 0.835207 | 467.000000 | 0.0 |
| 12 | max fps | 0.001503 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019257 | 468.000000 | 382.0 |
| 14 | max tnr | 0.835207 | 1.000000 | 0.0 |
| 15 | max fnr | 0.835207 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001503 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019257 | 1.000000 | 382.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.420098 | 8.958251 | 8.958251 | 0.538462 | 0.504051 | 0.538462 | 0.504051 | 0.089744 | 0.089744 | 795.825115 | 795.825115 | 0.084824 |
| 1 | 2 | 0.020036 | 0.383058 | 7.038626 | 7.998439 | 0.423077 | 0.399799 | 0.480769 | 0.451925 | 0.070513 | 0.160256 | 603.862590 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.356658 | 7.678501 | 7.891793 | 0.461538 | 0.369165 | 0.474359 | 0.424338 | 0.076923 | 0.237179 | 667.850099 | 689.179268 | 0.220372 |
| 3 | 4 | 0.040072 | 0.339660 | 6.398751 | 7.518532 | 0.384615 | 0.348283 | 0.451923 | 0.405325 | 0.064103 | 0.301282 | 539.875082 | 651.853222 | 0.277915 |
| 4 | 5 | 0.050090 | 0.318442 | 5.119001 | 7.038626 | 0.307692 | 0.329914 | 0.423077 | 0.390242 | 0.051282 | 0.352564 | 411.900066 | 603.862590 | 0.321818 |
| 5 | 6 | 0.100051 | 0.066631 | 3.079296 | 5.061502 | 0.185090 | 0.169035 | 0.304236 | 0.279781 | 0.153846 | 0.506410 | 207.929603 | 406.150225 | 0.432346 |
| 6 | 7 | 0.150013 | 0.052471 | 0.727056 | 3.617924 | 0.043702 | 0.057770 | 0.217466 | 0.205840 | 0.036325 | 0.542735 | -27.294399 | 261.792384 | 0.417838 |
| 7 | 8 | 0.200103 | 0.047546 | 0.938483 | 2.947204 | 0.056410 | 0.049742 | 0.177150 | 0.166766 | 0.047009 | 0.589744 | -6.151655 | 194.720384 | 0.414559 |
| 8 | 9 | 0.300026 | 0.042585 | 0.962280 | 2.286129 | 0.057841 | 0.044730 | 0.137414 | 0.126122 | 0.096154 | 0.685897 | -3.771999 | 128.612904 | 0.410549 |
| 9 | 10 | 0.400077 | 0.039141 | 0.512557 | 1.842594 | 0.030809 | 0.040761 | 0.110754 | 0.104775 | 0.051282 | 0.737179 | -48.744281 | 84.259374 | 0.358661 |
| 10 | 11 | 0.500000 | 0.036458 | 0.684288 | 1.611111 | 0.041131 | 0.037768 | 0.096840 | 0.091384 | 0.068376 | 0.805556 | -31.571199 | 61.111111 | 0.325096 |
| 11 | 12 | 0.600051 | 0.033970 | 0.597983 | 1.442184 | 0.035944 | 0.035230 | 0.086687 | 0.082021 | 0.059829 | 0.865385 | -40.201661 | 44.218421 | 0.282302 |
| 12 | 13 | 0.699974 | 0.031380 | 0.577368 | 1.318730 | 0.034704 | 0.032656 | 0.079266 | 0.074974 | 0.057692 | 0.923077 | -42.263200 | 31.872971 | 0.237370 |
| 13 | 14 | 0.800026 | 0.028596 | 0.256279 | 1.185859 | 0.015404 | 0.030076 | 0.071279 | 0.069359 | 0.025641 | 0.948718 | -74.372140 | 18.585936 | 0.158201 |
| 14 | 15 | 0.899949 | 0.024452 | 0.299376 | 1.087431 | 0.017995 | 0.026688 | 0.065363 | 0.064621 | 0.029915 | 0.978632 | -70.062400 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.001158 | 0.213565 | 1.000000 | 0.012837 | 0.019512 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.049211827018923786 RMSE: 0.22183738868577538 LogLoss: 0.19058176443538008 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311436 Residual deviance: 742.12539071137 AIC: 772.12539071137 AUC: 0.7678693195086638 AUCPR: 0.26315901082500653 Gini: 0.5357386390173275 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2752557534641599:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1761.0 | 69.0 | 0.0377 | (69.0/1830.0) |
| 1 | 1 | 69.0 | 48.0 | 0.5897 | (69.0/117.0) |
| 2 | Total | 1830.0 | 117.0 | 0.0709 | (138.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.275256 | 0.410256 | 97.0 |
| 1 | max f2 | 0.051355 | 0.423177 | 197.0 |
| 2 | max f0point5 | 0.283070 | 0.413005 | 94.0 |
| 3 | max accuracy | 0.637162 | 0.940421 | 0.0 |
| 4 | max precision | 0.637162 | 1.000000 | 0.0 |
| 5 | max recall | 0.018557 | 1.000000 | 376.0 |
| 6 | max specificity | 0.637162 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.275256 | 0.372551 | 97.0 |
| 8 | max min_per_class_accuracy | 0.041372 | 0.686885 | 244.0 |
| 9 | max mean_per_class_accuracy | 0.051355 | 0.713570 | 197.0 |
| 10 | max tns | 0.637162 | 1830.000000 | 0.0 |
| 11 | max fns | 0.637162 | 116.000000 | 0.0 |
| 12 | max fps | 0.001193 | 1830.000000 | 399.0 |
| 13 | max tps | 0.018557 | 117.000000 | 376.0 |
| 14 | max tnr | 0.637162 | 1.000000 | 0.0 |
| 15 | max fnr | 0.637162 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001193 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018557 | 1.000000 | 376.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.94 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.426618 | 7.488462 | 7.488462 | 0.450000 | 0.494430 | 0.450000 | 0.494430 | 0.076923 | 0.076923 | 648.846154 | 648.846154 | 0.070912 |
| 1 | 2 | 0.020031 | 0.386545 | 7.882591 | 7.680473 | 0.473684 | 0.402435 | 0.461538 | 0.449612 | 0.076923 | 0.153846 | 688.259109 | 668.047337 | 0.142371 |
| 2 | 3 | 0.030303 | 0.358334 | 7.488462 | 7.615385 | 0.450000 | 0.374612 | 0.457627 | 0.424188 | 0.076923 | 0.230769 | 648.846154 | 661.538462 | 0.213283 |
| 3 | 4 | 0.040062 | 0.333253 | 6.130904 | 7.253780 | 0.368421 | 0.345460 | 0.435897 | 0.405011 | 0.059829 | 0.290598 | 513.090418 | 625.378041 | 0.266555 |
| 4 | 5 | 0.050334 | 0.313862 | 4.992308 | 6.792255 | 0.300000 | 0.324124 | 0.408163 | 0.388503 | 0.051282 | 0.341880 | 399.230769 | 579.225536 | 0.310186 |
| 5 | 6 | 0.100154 | 0.067664 | 2.230241 | 4.522945 | 0.134021 | 0.157817 | 0.271795 | 0.273752 | 0.111111 | 0.452991 | 123.024055 | 352.294543 | 0.375396 |
| 6 | 7 | 0.149974 | 0.051659 | 1.887127 | 3.647348 | 0.113402 | 0.057564 | 0.219178 | 0.201936 | 0.094017 | 0.547009 | 88.712662 | 264.734809 | 0.422418 |
| 7 | 8 | 0.200308 | 0.047410 | 0.679226 | 2.901512 | 0.040816 | 0.049165 | 0.174359 | 0.163547 | 0.034188 | 0.581197 | -32.077446 | 190.151216 | 0.405240 |
| 8 | 9 | 0.299949 | 0.042497 | 0.772006 | 2.194108 | 0.046392 | 0.044596 | 0.131849 | 0.124033 | 0.076923 | 0.658120 | -22.799366 | 119.410783 | 0.381070 |
| 9 | 10 | 0.400103 | 0.039193 | 0.853386 | 1.858497 | 0.051282 | 0.040834 | 0.111682 | 0.103206 | 0.085470 | 0.743590 | -14.661407 | 85.849709 | 0.365448 |
| 10 | 11 | 0.500257 | 0.035943 | 0.682709 | 1.623098 | 0.041026 | 0.037558 | 0.097536 | 0.090063 | 0.068376 | 0.811966 | -31.729126 | 62.309798 | 0.331638 |
| 11 | 12 | 0.599897 | 0.033765 | 0.686228 | 1.467488 | 0.041237 | 0.034834 | 0.088185 | 0.080890 | 0.068376 | 0.880342 | -31.377214 | 46.748771 | 0.298375 |
| 12 | 13 | 0.700051 | 0.031250 | 0.170677 | 1.281957 | 0.010256 | 0.032562 | 0.077036 | 0.073976 | 0.017094 | 0.897436 | -82.932281 | 28.195722 | 0.210004 |
| 13 | 14 | 0.799692 | 0.028535 | 0.343114 | 1.164979 | 0.020619 | 0.029897 | 0.070006 | 0.068484 | 0.034188 | 0.931624 | -65.688607 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.024761 | 0.426693 | 1.082806 | 0.025641 | 0.026764 | 0.065068 | 0.063840 | 0.042735 | 0.974359 | -57.330703 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.001107 | 0.256016 | 1.000000 | 0.015385 | 0.019806 | 0.060092 | 0.059430 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04803644248427324 RMSE: 0.2191721754335464 LogLoss: 0.18656997599889774 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.967909721508 Residual deviance: 2905.2676662548356 AIC: 2935.2676662548356 AUC: 0.7615461407651897 AUCPR: 0.29123849831602355 Gini: 0.5230922815303793 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.11879024385608884:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6995.0 | 323.0 | 0.0441 | (323.0/7318.0) |
| 1 | 1 | 257.0 | 211.0 | 0.5491 | (257.0/468.0) |
| 2 | Total | 7252.0 | 534.0 | 0.0745 | (580.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.118790 | 0.421158 | 190.0 |
| 1 | max f2 | 0.069860 | 0.445487 | 221.0 |
| 2 | max f0point5 | 0.309459 | 0.425153 | 123.0 |
| 3 | max accuracy | 0.428967 | 0.940791 | 39.0 |
| 4 | max precision | 0.847362 | 1.000000 | 0.0 |
| 5 | max recall | 0.019095 | 1.000000 | 378.0 |
| 6 | max specificity | 0.847362 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.118790 | 0.382483 | 190.0 |
| 8 | max min_per_class_accuracy | 0.044134 | 0.683110 | 274.0 |
| 9 | max mean_per_class_accuracy | 0.064895 | 0.712813 | 227.0 |
| 10 | max tns | 0.847362 | 7318.000000 | 0.0 |
| 11 | max fns | 0.847362 | 467.000000 | 0.0 |
| 12 | max fps | 0.001348 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019095 | 468.000000 | 378.0 |
| 14 | max tnr | 0.847362 | 1.000000 | 0.0 |
| 15 | max fnr | 0.847362 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001348 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019095 | 1.000000 | 378.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.05 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.415984 | 8.531668 | 8.531668 | 0.512821 | 0.500843 | 0.512821 | 0.500843 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.375533 | 7.678501 | 8.105084 | 0.461538 | 0.394717 | 0.487179 | 0.447780 | 0.076923 | 0.162393 | 667.850099 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.350830 | 7.251918 | 7.820695 | 0.435897 | 0.362318 | 0.470085 | 0.419293 | 0.072650 | 0.235043 | 625.191760 | 682.069545 | 0.218098 |
| 3 | 4 | 0.040072 | 0.329055 | 7.038626 | 7.625178 | 0.423077 | 0.341386 | 0.458333 | 0.399816 | 0.070513 | 0.305556 | 603.862590 | 662.517806 | 0.282462 |
| 4 | 5 | 0.050090 | 0.303354 | 5.119001 | 7.123943 | 0.307692 | 0.316901 | 0.428205 | 0.383233 | 0.051282 | 0.356838 | 411.900066 | 612.394258 | 0.326365 |
| 5 | 6 | 0.100051 | 0.061954 | 2.822688 | 4.976076 | 0.169666 | 0.137261 | 0.299101 | 0.260405 | 0.141026 | 0.497863 | 182.268802 | 397.607606 | 0.423253 |
| 6 | 7 | 0.168764 | 0.060848 | 0.684128 | 3.228593 | 0.041121 | 0.060932 | 0.194064 | 0.179189 | 0.047009 | 0.544872 | -31.587187 | 222.859345 | 0.400160 |
| 7 | 8 | 0.202800 | 0.060584 | 0.753362 | 2.813181 | 0.045283 | 0.060587 | 0.169094 | 0.159284 | 0.025641 | 0.570513 | -24.663764 | 181.318101 | 0.391229 |
| 8 | 9 | 0.300026 | 0.046075 | 0.769203 | 2.150813 | 0.046235 | 0.051391 | 0.129281 | 0.124321 | 0.074786 | 0.645299 | -23.079746 | 115.081299 | 0.367354 |
| 9 | 10 | 0.400077 | 0.041428 | 0.854262 | 1.826571 | 0.051348 | 0.043554 | 0.109791 | 0.104123 | 0.085470 | 0.730769 | -14.573802 | 82.657118 | 0.351841 |
| 10 | 11 | 0.500000 | 0.038042 | 0.470448 | 1.555556 | 0.028278 | 0.039607 | 0.093501 | 0.091229 | 0.047009 | 0.777778 | -52.955200 | 55.555556 | 0.295542 |
| 11 | 12 | 0.600051 | 0.035055 | 0.747479 | 1.420819 | 0.044929 | 0.036528 | 0.085402 | 0.082109 | 0.074786 | 0.852564 | -25.252076 | 42.081852 | 0.268661 |
| 12 | 13 | 0.699974 | 0.032299 | 0.449064 | 1.282098 | 0.026992 | 0.033711 | 0.077064 | 0.075200 | 0.044872 | 0.897436 | -55.093600 | 28.209833 | 0.210090 |
| 13 | 14 | 0.800026 | 0.029482 | 0.427131 | 1.175176 | 0.025674 | 0.030922 | 0.070637 | 0.069662 | 0.042735 | 0.940171 | -57.286901 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.025191 | 0.342144 | 1.082683 | 0.020566 | 0.027503 | 0.065078 | 0.064981 | 0.034188 | 0.974359 | -65.785600 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.001125 | 0.256279 | 1.000000 | 0.015404 | 0.020233 | 0.060108 | 0.060504 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9326928 | 0.022330763 | 0.95 | 0.9269231 | 0.9692308 | 0.9153846 | 0.95 | 0.93846154 | 0.9307692 | 0.95384616 | 0.95 | 0.9269231 | 0.9076923 | 0.9230769 | 0.97307694 | 0.9346154 | 0.91923076 | 0.9076923 | 0.9227799 | 0.8841699 | 0.9498069 | 0.9227799 | 0.9459459 | 0.94208497 | 0.95366794 | 0.8918919 | 0.9150579 | 0.9034749 | 0.95366794 | 0.96525097 | 0.93822396 | 0.9150579 |
| 1 | auc | 0.75830376 | 0.10353858 | 0.81505686 | 0.75563526 | 0.81672144 | 0.4488712 | 0.7644628 | 0.8413978 | 0.696281 | 0.8430396 | 0.79679143 | 0.77074796 | 0.8177021 | 0.70064276 | 0.8692876 | 0.7646004 | 0.841957 | 0.720964 | 0.82693243 | 0.8381197 | 0.51686746 | 0.6898907 | 0.8090176 | 0.7975708 | 0.85915166 | 0.81950206 | 0.562753 | 0.78986466 | 0.6467611 | 0.8137778 | 0.84237534 | 0.6723684 |
| 2 | err | 0.0673072 | 0.022330763 | 0.05 | 0.073076926 | 0.03076923 | 0.08461539 | 0.05 | 0.06153846 | 0.06923077 | 0.046153847 | 0.05 | 0.073076926 | 0.092307694 | 0.07692308 | 0.026923077 | 0.06538462 | 0.08076923 | 0.092307694 | 0.077220075 | 0.115830116 | 0.05019305 | 0.077220075 | 0.054054055 | 0.057915058 | 0.046332046 | 0.10810811 | 0.08494209 | 0.096525095 | 0.046332046 | 0.034749035 | 0.06177606 | 0.08494209 |
| 3 | err_count | 17.466667 | 5.7878194 | 13.0 | 19.0 | 8.0 | 22.0 | 13.0 | 16.0 | 18.0 | 12.0 | 13.0 | 19.0 | 24.0 | 20.0 | 7.0 | 17.0 | 21.0 | 24.0 | 20.0 | 30.0 | 13.0 | 20.0 | 14.0 | 15.0 | 12.0 | 28.0 | 22.0 | 25.0 | 12.0 | 9.0 | 16.0 | 22.0 |
| 4 | f0point5 | 0.45632464 | 0.1489134 | 0.61538464 | 0.4347826 | 0.65217394 | 0.12987013 | 0.6481481 | 0.39473686 | 0.47297296 | 0.5555556 | 0.71428573 | 0.36764705 | 0.53691274 | 0.4054054 | 0.7291667 | 0.47619048 | 0.43103448 | 0.23255815 | 0.43010753 | 0.46242774 | 0.2631579 | 0.3846154 | 0.443038 | 0.45454547 | 0.6756757 | 0.36231884 | 0.14705883 | 0.52238804 | 0.41666666 | 0.5263158 | 0.40229884 | 0.40229884 |
| 5 | f1 | 0.4515757 | 0.13369487 | 0.55172414 | 0.45714286 | 0.42857143 | 0.15384616 | 0.5185185 | 0.42857143 | 0.4375 | 0.6 | 0.68292683 | 0.3448276 | 0.5714286 | 0.375 | 0.6666667 | 0.4848485 | 0.4878049 | 0.25 | 0.44444445 | 0.516129 | 0.23529412 | 0.4117647 | 0.5 | 0.516129 | 0.625 | 0.41666666 | 0.15384616 | 0.5283019 | 0.33333334 | 0.5714286 | 0.46666667 | 0.3888889 |
| 6 | f2 | 0.46001008 | 0.14310712 | 0.5 | 0.48192772 | 0.31914893 | 0.18867925 | 0.43209878 | 0.46875 | 0.40697673 | 0.65217394 | 0.6542056 | 0.32467532 | 0.610687 | 0.3488372 | 0.61403507 | 0.49382716 | 0.56179774 | 0.27027026 | 0.4597701 | 0.5839416 | 0.21276596 | 0.443038 | 0.57377046 | 0.5970149 | 0.5813953 | 0.49019608 | 0.16129032 | 0.53435117 | 0.2777778 | 0.625 | 0.5555556 | 0.37634408 |
| 7 | lift_top_group | 7.5016413 | 5.8846664 | 10.196078 | 0.0 | 23.636364 | 0.0 | 14.444445 | 14.444445 | 4.814815 | 6.6666665 | 7.878788 | 5.4166665 | 6.9333334 | 0.0 | 21.666666 | 10.833333 | 10.833333 | 0.0 | 5.0784316 | 6.9066668 | 0.0 | 5.7555556 | 7.848485 | 7.1944447 | 4.796296 | 4.796296 | 0.0 | 6.6410255 | 7.1944447 | 9.592592 | 7.848485 | 13.631579 |
| 8 | logloss | 0.18462397 | 0.039543774 | 0.17875844 | 0.19146298 | 0.14266707 | 0.1557166 | 0.2019223 | 0.14577305 | 0.22020625 | 0.13625705 | 0.19749188 | 0.20571235 | 0.23910646 | 0.22634174 | 0.12375259 | 0.18867873 | 0.17415515 | 0.19782217 | 0.19603881 | 0.2616045 | 0.16554345 | 0.1919738 | 0.14072643 | 0.14539239 | 0.18313527 | 0.20373861 | 0.18631649 | 0.27821544 | 0.17243499 | 0.11832367 | 0.1382651 | 0.2311856 |
| 9 | max_per_class_error | 0.5276719 | 0.15983774 | 0.5294118 | 0.5 | 0.72727275 | 0.7777778 | 0.6111111 | 0.5 | 0.6111111 | 0.30769232 | 0.36363637 | 0.6875 | 0.36 | 0.6666667 | 0.41666666 | 0.5 | 0.375 | 0.71428573 | 0.5294118 | 0.36 | 0.8 | 0.53333336 | 0.36363637 | 0.33333334 | 0.44444445 | 0.44444445 | 0.8333333 | 0.46153846 | 0.75 | 0.33333334 | 0.36363637 | 0.6315789 |
| 10 | mcc | 0.42691565 | 0.14029798 | 0.5350431 | 0.4200517 | 0.5140406 | 0.12013799 | 0.5285456 | 0.40134212 | 0.404828 | 0.58181274 | 0.658022 | 0.30842358 | 0.52415067 | 0.3377009 | 0.6602816 | 0.4502108 | 0.45937127 | 0.20344673 | 0.4038083 | 0.46437165 | 0.21376128 | 0.37402478 | 0.4853625 | 0.50157905 | 0.6061117 | 0.37551796 | 0.10976955 | 0.47466463 | 0.3323641 | 0.5599354 | 0.4547783 | 0.3440105 |
| 11 | mean_per_class_accuracy | 0.71675235 | 0.07807255 | 0.72706366 | 0.727459 | 0.6363636 | 0.58123064 | 0.6903122 | 0.7298387 | 0.67998165 | 0.8299595 | 0.8076776 | 0.6398566 | 0.7880851 | 0.6501377 | 0.78763443 | 0.73155737 | 0.7817623 | 0.6144019 | 0.71256685 | 0.7751282 | 0.58995986 | 0.70874316 | 0.79802054 | 0.81106615 | 0.76947904 | 0.736284 | 0.55904186 | 0.7413338 | 0.6189271 | 0.82133335 | 0.7939883 | 0.66337717 |
| 12 | mean_per_class_error | 0.28324762 | 0.07807255 | 0.27293634 | 0.272541 | 0.36363637 | 0.41876936 | 0.3096878 | 0.2701613 | 0.32001835 | 0.17004049 | 0.19232239 | 0.36014345 | 0.2119149 | 0.34986225 | 0.2123656 | 0.26844263 | 0.2182377 | 0.38559815 | 0.28743315 | 0.2248718 | 0.41004017 | 0.29125684 | 0.20197947 | 0.18893388 | 0.23052098 | 0.263716 | 0.44095817 | 0.25866622 | 0.38107288 | 0.17866667 | 0.20601173 | 0.3366228 |
| 13 | mse | 0.047556333 | 0.012442005 | 0.04601631 | 0.050186362 | 0.033994347 | 0.034035698 | 0.051176757 | 0.03647416 | 0.057041984 | 0.034926727 | 0.05188797 | 0.054004412 | 0.06654503 | 0.05953663 | 0.028948255 | 0.04991504 | 0.046642862 | 0.04944792 | 0.052471895 | 0.073745035 | 0.037307356 | 0.049006004 | 0.035179555 | 0.036987964 | 0.048577428 | 0.05610517 | 0.044170078 | 0.07767419 | 0.041465987 | 0.027663352 | 0.03553915 | 0.06001641 |
| 14 | null_deviance | 118.0656 | 25.645779 | 125.73113 | 120.223526 | 92.82863 | 81.936905 | 131.24835 | 98.28862 | 131.24835 | 103.75808 | 153.41394 | 120.223526 | 170.14055 | 131.24835 | 98.28862 | 120.223526 | 120.223526 | 109.23703 | 125.607956 | 170.02197 | 87.25086 | 114.60118 | 92.701996 | 98.16257 | 131.12575 | 131.12575 | 98.16257 | 175.61775 | 98.16257 | 81.80913 | 92.701996 | 136.65318 |
| 15 | pr_auc | 0.30297047 | 0.13891171 | 0.43973702 | 0.20115618 | 0.3761456 | 0.042780653 | 0.46995047 | 0.37787232 | 0.27951434 | 0.331688 | 0.5249308 | 0.19426824 | 0.47123793 | 0.19365373 | 0.6138169 | 0.31494454 | 0.33821762 | 0.11985888 | 0.28213194 | 0.45137498 | 0.063309394 | 0.22525942 | 0.26478264 | 0.26436722 | 0.4366993 | 0.30299166 | 0.059275545 | 0.38788408 | 0.21590279 | 0.2886433 | 0.21771228 | 0.3390059 |
| 16 | precision | 0.47179735 | 0.19070517 | 0.6666667 | 0.42105263 | 1.0 | 0.11764706 | 0.7777778 | 0.375 | 0.5 | 0.5294118 | 0.7368421 | 0.3846154 | 0.516129 | 0.42857143 | 0.7777778 | 0.47058824 | 0.4 | 0.22222222 | 0.42105263 | 0.43243244 | 0.2857143 | 0.36842105 | 0.4117647 | 0.42105263 | 0.71428573 | 0.33333334 | 0.14285715 | 0.5185185 | 0.5 | 0.5 | 0.36842105 | 0.4117647 |
| 17 | r2 | 0.1459115 | 0.08926461 | 0.2469856 | 0.13099432 | 0.16100112 | -0.018509619 | 0.20579687 | 0.1714875 | 0.11477543 | 0.26470047 | 0.33009416 | 0.064882636 | 0.2343074 | 0.07606152 | 0.34243885 | 0.13569245 | 0.19235209 | 0.029419461 | 0.14441735 | 0.15437767 | -0.0050661727 | 0.10181096 | 0.13494147 | 0.16289145 | 0.24881919 | 0.13241333 | 3.4646806E-4 | 0.1399039 | 0.061545286 | 0.17525095 | 0.12609908 | 0.117113866 |
| 18 | recall | 0.47232813 | 0.15983774 | 0.47058824 | 0.5 | 0.27272728 | 0.22222222 | 0.3888889 | 0.5 | 0.3888889 | 0.6923077 | 0.6363636 | 0.3125 | 0.64 | 0.33333334 | 0.5833333 | 0.5 | 0.625 | 0.2857143 | 0.47058824 | 0.64 | 0.2 | 0.46666667 | 0.6363636 | 0.6666667 | 0.5555556 | 0.5555556 | 0.16666667 | 0.53846157 | 0.25 | 0.6666667 | 0.6363636 | 0.36842105 |
| 19 | residual_deviance | 95.83028 | 20.505869 | 92.954384 | 99.56075 | 74.186874 | 80.97263 | 104.999596 | 75.80199 | 114.50725 | 70.85367 | 102.69578 | 106.97042 | 124.33536 | 117.69771 | 64.35135 | 98.11294 | 90.56068 | 102.86753 | 101.54811 | 135.51112 | 85.75151 | 99.44243 | 72.89629 | 75.313255 | 94.86407 | 105.536606 | 96.51195 | 144.11559 | 89.32133 | 61.29166 | 71.62132 | 119.75414 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:12:55 | 0.000 sec | 2 | .86E1 | 15 | 0.452067 | 0.452168 | 0.452563 | 0.017894 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:12:55 | 0.003 sec | 4 | .54E1 | 15 | 0.450600 | 0.450812 | 0.451166 | 0.017806 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:12:55 | 0.006 sec | 6 | .33E1 | 15 | 0.448295 | 0.448682 | 0.448967 | 0.017667 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:12:55 | 0.008 sec | 8 | .21E1 | 15 | 0.444710 | 0.445371 | 0.445543 | 0.017454 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:12:55 | 0.011 sec | 10 | .13E1 | 15 | 0.439305 | 0.440383 | 0.440368 | 0.017138 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:12:55 | 0.013 sec | 12 | .8E0 | 15 | 0.431509 | 0.433199 | 0.432872 | 0.016692 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:12:55 | 0.016 sec | 14 | .49E0 | 15 | 0.421105 | 0.423640 | 0.422799 | 0.016119 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:12:55 | 0.018 sec | 16 | .31E0 | 15 | 0.408899 | 0.412491 | 0.410857 | 0.015491 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:12:55 | 0.020 sec | 18 | .19E0 | 15 | 0.396871 | 0.401643 | 0.398962 | 0.014940 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:12:55 | 0.023 sec | 20 | .12E0 | 15 | 0.387052 | 0.393012 | 0.389202 | 0.014568 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:12:55 | 0.026 sec | 22 | .74E-1 | 15 | 0.380162 | 0.387258 | 0.382399 | 0.014374 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:12:55 | 0.028 sec | 24 | .46E-1 | 15 | 0.375758 | 0.383918 | 0.378165 | 0.014300 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:12:55 | 0.031 sec | 26 | .28E-1 | 15 | 0.373060 | 0.382196 | 0.375698 | 0.014291 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:12:55 | 0.033 sec | 28 | .18E-1 | 15 | 0.371421 | 0.381423 | 0.374010 | 0.014346 | 0.0 | 28.0 | 0.219063 | 0.185215 | 0.150568 | 0.77782 | 0.29412 | 8.958251 | 0.078988 | 0.221837 | 0.190582 | 0.128707 | 0.767869 | 0.263159 | 7.488462 | 0.070878 | |
| 14 | 2021-07-15 20:12:55 | 0.036 sec | 30 | .11E-1 | 15 | 0.370431 | 0.381164 | 0.373146 | 0.014389 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:12:55 | 0.039 sec | 32 | .68E-2 | 15 | 0.369845 | 0.381154 | 0.373293 | 0.014486 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:12:55 | 0.042 sec | 34 | .42E-2 | 15 | 0.369513 | 0.381247 | 0.374363 | 0.014865 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:12:55 | 0.044 sec | 36 | .26E-2 | 15 | 0.369333 | 0.381363 | 0.376752 | 0.015017 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.551262 | 1.000000 | 0.269644 |
| 1 | Average_Transaction_Frequency | 0.258506 | 0.468936 | 0.126446 |
| 2 | Merchant_ID | 0.203099 | 0.368426 | 0.099344 |
| 3 | Minimum_Transaction_Amount | 0.173700 | 0.315096 | 0.084964 |
| 4 | Channel_ID | 0.168334 | 0.305360 | 0.082339 |
| 5 | Card_Type.1 | 0.140428 | 0.254740 | 0.068689 |
| 6 | Card_Type.0 | 0.139046 | 0.252233 | 0.068013 |
| 7 | Transaction_Amount | 0.138304 | 0.250887 | 0.067650 |
| 8 | Transaction_Date | 0.072975 | 0.132378 | 0.035695 |
| 9 | Average_Transaction_Amount | 0.058965 | 0.106964 | 0.028842 |
| 10 | Month | 0.047349 | 0.085891 | 0.023160 |
| 11 | Day | 0.035071 | 0.063620 | 0.017155 |
| 12 | Maximum_Transaction_Amount | 0.029411 | 0.053351 | 0.014386 |
| 13 | City_ID | 0.027953 | 0.050708 | 0.013673 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201258 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01079 ) | nlambda = 30, lambda.max = 8.5008, lambda.min = 0.01079, lambda.1s... | 14 | 14 | 30 | automl_training_py_313_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04844180239263909 RMSE: 0.22009498493295818 LogLoss: 0.186887129587275 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938366 Residual deviance: 2910.206381933046 AIC: 2940.206381933046 AUC: 0.7827072281670533 AUCPR: 0.28214040640634186 Gini: 0.5654144563341066 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.23758245062368546:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7009.0 | 309.0 | 0.0422 | (309.0/7318.0) |
| 1 | 1 | 273.0 | 195.0 | 0.5833 | (273.0/468.0) |
| 2 | Total | 7282.0 | 504.0 | 0.0747 | (582.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.237582 | 0.401235 | 155.0 |
| 1 | max f2 | 0.070040 | 0.440893 | 220.0 |
| 2 | max f0point5 | 0.318995 | 0.415029 | 114.0 |
| 3 | max accuracy | 0.435778 | 0.940021 | 36.0 |
| 4 | max precision | 0.435778 | 0.509434 | 36.0 |
| 5 | max recall | 0.017176 | 1.000000 | 384.0 |
| 6 | max specificity | 0.816476 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.237582 | 0.361712 | 155.0 |
| 8 | max min_per_class_accuracy | 0.043096 | 0.698718 | 285.0 |
| 9 | max mean_per_class_accuracy | 0.046780 | 0.714684 | 269.0 |
| 10 | max tns | 0.816476 | 7317.000000 | 0.0 |
| 11 | max fns | 0.816476 | 468.000000 | 0.0 |
| 12 | max fps | 0.000915 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017176 | 468.000000 | 384.0 |
| 14 | max tnr | 0.816476 | 0.999863 | 0.0 |
| 15 | max fnr | 0.816476 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000915 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017176 | 1.000000 | 384.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.418985 | 7.891793 | 7.891793 | 0.474359 | 0.486391 | 0.474359 | 0.486391 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.386831 | 8.318376 | 8.105084 | 0.500000 | 0.400731 | 0.487179 | 0.443561 | 0.083333 | 0.162393 | 731.837607 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.362315 | 7.251918 | 7.820695 | 0.435897 | 0.372728 | 0.470085 | 0.419950 | 0.072650 | 0.235043 | 625.191760 | 682.069545 | 0.218098 |
| 3 | 4 | 0.040072 | 0.344116 | 5.972167 | 7.358563 | 0.358974 | 0.352894 | 0.442308 | 0.403186 | 0.059829 | 0.294872 | 497.216743 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.318791 | 6.185459 | 7.123943 | 0.371795 | 0.331810 | 0.428205 | 0.388911 | 0.061966 | 0.356838 | 518.545913 | 612.394258 | 0.326365 |
| 5 | 6 | 0.100051 | 0.065997 | 2.694384 | 4.912006 | 0.161954 | 0.153470 | 0.295250 | 0.271342 | 0.134615 | 0.491453 | 169.438402 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.052625 | 1.069200 | 3.632168 | 0.064267 | 0.057679 | 0.218322 | 0.200182 | 0.053419 | 0.544872 | 6.920001 | 263.216763 | 0.420111 |
| 7 | 8 | 0.200103 | 0.048344 | 0.981142 | 2.968560 | 0.058974 | 0.050378 | 0.178434 | 0.162683 | 0.049145 | 0.594017 | -1.885821 | 196.856039 | 0.419106 |
| 8 | 9 | 0.300026 | 0.043661 | 0.940896 | 2.293251 | 0.056555 | 0.045790 | 0.137842 | 0.123752 | 0.094017 | 0.688034 | -5.910399 | 129.325094 | 0.412822 |
| 9 | 10 | 0.400077 | 0.040479 | 0.555270 | 1.858616 | 0.033376 | 0.042021 | 0.111717 | 0.103312 | 0.055556 | 0.743590 | -44.472971 | 85.861629 | 0.365481 |
| 10 | 11 | 0.500000 | 0.037891 | 0.684288 | 1.623932 | 0.041131 | 0.039177 | 0.097611 | 0.090495 | 0.068376 | 0.811966 | -31.571199 | 62.393162 | 0.331917 |
| 11 | 12 | 0.600051 | 0.035289 | 0.662053 | 1.463550 | 0.039795 | 0.036593 | 0.087971 | 0.081508 | 0.066239 | 0.878205 | -33.794696 | 46.354990 | 0.295942 |
| 12 | 13 | 0.699974 | 0.032790 | 0.449064 | 1.318730 | 0.026992 | 0.034053 | 0.079266 | 0.074733 | 0.044872 | 0.923077 | -55.093600 | 31.872971 | 0.237370 |
| 13 | 14 | 0.800026 | 0.029570 | 0.341705 | 1.196543 | 0.020539 | 0.031241 | 0.071922 | 0.069294 | 0.034188 | 0.957265 | -65.829521 | 19.654278 | 0.167295 |
| 14 | 15 | 0.899949 | 0.024781 | 0.235224 | 1.089806 | 0.014139 | 0.027410 | 0.065506 | 0.064644 | 0.023504 | 0.980769 | -76.477600 | 8.980580 | 0.085989 |
| 15 | 16 | 1.000000 | 0.000712 | 0.192209 | 1.000000 | 0.011553 | 0.019309 | 0.060108 | 0.060108 | 0.019231 | 1.000000 | -80.779105 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.047107024437124154 RMSE: 0.21704152698763468 LogLoss: 0.18379445841305286 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311417 Residual deviance: 715.6956210604279 AIC: 745.6956210604279 AUC: 0.7482252113399654 AUCPR: 0.31412322365875744 Gini: 0.4964504226799309 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2097979265896012:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1740.0 | 90.0 | 0.0492 | (90.0/1830.0) |
| 1 | 1 | 59.0 | 58.0 | 0.5043 | (59.0/117.0) |
| 2 | Total | 1799.0 | 148.0 | 0.0765 | (149.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.209798 | 0.437736 | 121.0 |
| 1 | max f2 | 0.160381 | 0.473515 | 128.0 |
| 2 | max f0point5 | 0.352952 | 0.427553 | 64.0 |
| 3 | max accuracy | 0.416263 | 0.942989 | 25.0 |
| 4 | max precision | 0.744415 | 1.000000 | 0.0 |
| 5 | max recall | 0.019493 | 1.000000 | 372.0 |
| 6 | max specificity | 0.744415 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.209798 | 0.400441 | 121.0 |
| 8 | max min_per_class_accuracy | 0.042230 | 0.675214 | 245.0 |
| 9 | max mean_per_class_accuracy | 0.079075 | 0.728443 | 148.0 |
| 10 | max tns | 0.744415 | 1830.000000 | 0.0 |
| 11 | max fns | 0.744415 | 116.000000 | 0.0 |
| 12 | max fps | 0.001068 | 1830.000000 | 399.0 |
| 13 | max tps | 0.019493 | 117.000000 | 372.0 |
| 14 | max tnr | 0.744415 | 1.000000 | 0.0 |
| 15 | max fnr | 0.744415 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001068 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019493 | 1.000000 | 372.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.26 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.433782 | 9.984615 | 9.984615 | 0.600000 | 0.511340 | 0.600000 | 0.511340 | 0.102564 | 0.102564 | 898.461538 | 898.461538 | 0.098193 |
| 1 | 2 | 0.020031 | 0.397229 | 7.006748 | 8.533859 | 0.421053 | 0.413241 | 0.512821 | 0.463548 | 0.068376 | 0.170940 | 600.674764 | 753.385930 | 0.160558 |
| 2 | 3 | 0.030303 | 0.368477 | 5.824359 | 7.615385 | 0.350000 | 0.380258 | 0.457627 | 0.435314 | 0.059829 | 0.230769 | 482.435897 | 661.538462 | 0.213283 |
| 3 | 4 | 0.040062 | 0.348434 | 7.882591 | 7.680473 | 0.473684 | 0.358558 | 0.461538 | 0.416617 | 0.076923 | 0.307692 | 688.259109 | 668.047337 | 0.284741 |
| 4 | 5 | 0.050334 | 0.326957 | 3.328205 | 6.792255 | 0.200000 | 0.339004 | 0.408163 | 0.400778 | 0.034188 | 0.341880 | 232.820513 | 579.225536 | 0.310186 |
| 5 | 6 | 0.100154 | 0.069085 | 3.602696 | 5.205654 | 0.216495 | 0.195309 | 0.312821 | 0.298570 | 0.179487 | 0.521368 | 260.269627 | 420.565417 | 0.448143 |
| 6 | 7 | 0.149974 | 0.053062 | 0.514671 | 3.647348 | 0.030928 | 0.059613 | 0.219178 | 0.219191 | 0.025641 | 0.547009 | -48.532910 | 264.734809 | 0.422418 |
| 7 | 8 | 0.200308 | 0.048190 | 0.509419 | 2.858843 | 0.030612 | 0.050213 | 0.171795 | 0.176730 | 0.025641 | 0.572650 | -49.058085 | 185.884287 | 0.396147 |
| 8 | 9 | 0.299949 | 0.043230 | 0.772006 | 2.165613 | 0.046392 | 0.045238 | 0.130137 | 0.133049 | 0.076923 | 0.649573 | -22.799366 | 116.561293 | 0.371977 |
| 9 | 10 | 0.400103 | 0.039893 | 0.597370 | 1.773049 | 0.035897 | 0.041483 | 0.106547 | 0.110128 | 0.059829 | 0.709402 | -40.262985 | 77.304895 | 0.329074 |
| 10 | 11 | 0.500257 | 0.037330 | 0.682709 | 1.554757 | 0.041026 | 0.038553 | 0.093429 | 0.095799 | 0.068376 | 0.777778 | -31.729126 | 55.475702 | 0.295264 |
| 11 | 12 | 0.599897 | 0.034871 | 0.514671 | 1.382003 | 0.030928 | 0.036107 | 0.083048 | 0.085884 | 0.051282 | 0.829060 | -48.532910 | 38.200299 | 0.243814 |
| 12 | 13 | 0.700051 | 0.032346 | 0.341354 | 1.233121 | 0.020513 | 0.033639 | 0.074101 | 0.078410 | 0.034188 | 0.863248 | -65.864563 | 23.312076 | 0.173630 |
| 13 | 14 | 0.799692 | 0.029142 | 0.171557 | 1.100851 | 0.010309 | 0.030810 | 0.066153 | 0.072479 | 0.017094 | 0.880342 | -82.844303 | 10.085141 | 0.085806 |
| 14 | 15 | 0.899846 | 0.024780 | 0.768047 | 1.063810 | 0.046154 | 0.027125 | 0.063927 | 0.067431 | 0.076923 | 0.957265 | -23.195266 | 6.380986 | 0.061090 |
| 15 | 16 | 1.000000 | 0.000919 | 0.426693 | 1.000000 | 0.025641 | 0.019308 | 0.060092 | 0.062611 | 0.042735 | 1.000000 | -57.330703 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048617361404687386 RMSE: 0.22049344979995977 LogLoss: 0.18813286299828305 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.080749427056 Residual deviance: 2929.6049426092636 AIC: 2959.6049426092636 AUC: 0.7691414215737801 AUCPR: 0.27477993098712233 Gini: 0.5382828431475601 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.24324210936346732:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7021.0 | 297.0 | 0.0406 | (297.0/7318.0) |
| 1 | 1 | 276.0 | 192.0 | 0.5897 | (276.0/468.0) |
| 2 | Total | 7297.0 | 489.0 | 0.0736 | (573.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.243242 | 0.401254 | 152.0 |
| 1 | max f2 | 0.067364 | 0.433448 | 223.0 |
| 2 | max f0point5 | 0.311100 | 0.413405 | 121.0 |
| 3 | max accuracy | 0.428398 | 0.940149 | 39.0 |
| 4 | max precision | 0.428398 | 0.517241 | 39.0 |
| 5 | max recall | 0.015415 | 1.000000 | 386.0 |
| 6 | max specificity | 0.835594 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.243242 | 0.362167 | 152.0 |
| 8 | max min_per_class_accuracy | 0.043587 | 0.693359 | 281.0 |
| 9 | max mean_per_class_accuracy | 0.063956 | 0.707328 | 228.0 |
| 10 | max tns | 0.835594 | 7317.000000 | 0.0 |
| 11 | max fns | 0.835594 | 468.000000 | 0.0 |
| 12 | max fps | 0.000882 | 7318.000000 | 399.0 |
| 13 | max tps | 0.015415 | 468.000000 | 386.0 |
| 14 | max tnr | 0.835594 | 0.999863 | 0.0 |
| 15 | max fnr | 0.835594 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000882 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015415 | 1.000000 | 386.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.04 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.414416 | 7.251918 | 7.251918 | 0.435897 | 0.487587 | 0.435897 | 0.487587 | 0.072650 | 0.072650 | 625.191760 | 625.191760 | 0.066637 |
| 1 | 2 | 0.020036 | 0.381201 | 8.744959 | 7.998439 | 0.525641 | 0.397184 | 0.480769 | 0.442386 | 0.087607 | 0.160256 | 774.495946 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.358063 | 7.465209 | 7.820695 | 0.448718 | 0.369326 | 0.470085 | 0.418032 | 0.074786 | 0.235043 | 646.520929 | 682.069545 | 0.218098 |
| 3 | 4 | 0.040072 | 0.340587 | 6.185459 | 7.411886 | 0.371795 | 0.349374 | 0.445513 | 0.400868 | 0.061966 | 0.297009 | 518.545913 | 641.188637 | 0.273368 |
| 4 | 5 | 0.050090 | 0.312210 | 5.972167 | 7.123943 | 0.358974 | 0.327952 | 0.428205 | 0.386285 | 0.059829 | 0.356838 | 497.216743 | 612.394258 | 0.326365 |
| 5 | 6 | 0.100051 | 0.064180 | 2.608848 | 4.869293 | 0.156812 | 0.145804 | 0.292683 | 0.266199 | 0.130342 | 0.487179 | 160.884802 | 386.929331 | 0.411886 |
| 6 | 7 | 0.150013 | 0.058561 | 1.026432 | 3.589436 | 0.061697 | 0.060568 | 0.215753 | 0.197714 | 0.051282 | 0.538462 | 2.643201 | 258.943625 | 0.413291 |
| 7 | 8 | 0.200103 | 0.050704 | 0.767850 | 2.883134 | 0.046154 | 0.053816 | 0.173299 | 0.161693 | 0.038462 | 0.576923 | -23.214990 | 188.313420 | 0.400919 |
| 8 | 9 | 0.300026 | 0.044598 | 0.983664 | 2.250520 | 0.059126 | 0.047223 | 0.135274 | 0.123569 | 0.098291 | 0.675214 | -1.633599 | 125.051955 | 0.399182 |
| 9 | 10 | 0.400077 | 0.041126 | 0.533914 | 1.821230 | 0.032092 | 0.042802 | 0.109470 | 0.103371 | 0.053419 | 0.728632 | -46.608626 | 82.123033 | 0.349567 |
| 10 | 11 | 0.500000 | 0.038404 | 0.641520 | 1.585470 | 0.038560 | 0.039735 | 0.095299 | 0.090653 | 0.064103 | 0.792735 | -35.847999 | 58.547009 | 0.311456 |
| 11 | 12 | 0.600051 | 0.035779 | 0.662053 | 1.431501 | 0.039795 | 0.037072 | 0.086045 | 0.081719 | 0.066239 | 0.858974 | -33.794696 | 43.150136 | 0.275482 |
| 12 | 13 | 0.699974 | 0.033117 | 0.513216 | 1.300414 | 0.030848 | 0.034472 | 0.078165 | 0.074975 | 0.051282 | 0.910256 | -48.678400 | 30.041402 | 0.223730 |
| 13 | 14 | 0.800026 | 0.029818 | 0.341705 | 1.180518 | 0.020539 | 0.031600 | 0.070958 | 0.069550 | 0.034188 | 0.944444 | -65.829521 | 18.051765 | 0.153655 |
| 14 | 15 | 0.899949 | 0.025261 | 0.320760 | 1.085057 | 0.019280 | 0.027749 | 0.065220 | 0.064909 | 0.032051 | 0.976496 | -67.924000 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.000694 | 0.234922 | 1.000000 | 0.014121 | 0.019565 | 0.060108 | 0.060372 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93282795 | 0.025121553 | 0.9461538 | 0.9423077 | 0.9423077 | 0.95384616 | 0.9307692 | 0.93846154 | 0.9230769 | 0.96153843 | 0.9115385 | 0.9269231 | 0.9423077 | 0.96153843 | 0.9076923 | 0.85 | 0.95 | 0.93846154 | 0.93436295 | 0.9459459 | 0.9459459 | 0.9266409 | 0.9266409 | 0.9498069 | 0.9459459 | 0.9266409 | 0.9459459 | 0.9266409 | 0.93822396 | 0.85714287 | 0.93436295 | 0.95366794 |
| 1 | auc | 0.76922524 | 0.06871129 | 0.8040816 | 0.78597945 | 0.7872937 | 0.921314 | 0.8252083 | 0.6999328 | 0.7450639 | 0.7864184 | 0.6109175 | 0.6898531 | 0.84996724 | 0.7676742 | 0.7567175 | 0.75986445 | 0.7865854 | 0.82505125 | 0.8417671 | 0.8990318 | 0.77201164 | 0.7618084 | 0.778561 | 0.7521858 | 0.6959428 | 0.77623457 | 0.6817982 | 0.8502189 | 0.6854424 | 0.70163935 | 0.68421054 | 0.7939815 |
| 2 | err | 0.067172065 | 0.025121553 | 0.053846154 | 0.057692308 | 0.057692308 | 0.046153847 | 0.06923077 | 0.06153846 | 0.07692308 | 0.03846154 | 0.08846154 | 0.073076926 | 0.057692308 | 0.03846154 | 0.092307694 | 0.15 | 0.05 | 0.06153846 | 0.06563707 | 0.054054055 | 0.054054055 | 0.07335907 | 0.07335907 | 0.05019305 | 0.054054055 | 0.07335907 | 0.054054055 | 0.07335907 | 0.06177606 | 0.14285715 | 0.06563707 | 0.046332046 |
| 3 | err_count | 17.433332 | 6.521494 | 14.0 | 15.0 | 15.0 | 12.0 | 18.0 | 16.0 | 20.0 | 10.0 | 23.0 | 19.0 | 15.0 | 10.0 | 24.0 | 39.0 | 13.0 | 16.0 | 17.0 | 14.0 | 14.0 | 19.0 | 19.0 | 13.0 | 14.0 | 19.0 | 14.0 | 19.0 | 16.0 | 37.0 | 17.0 | 12.0 |
| 4 | f0point5 | 0.4684475 | 0.14446397 | 0.52238804 | 0.5813953 | 0.3773585 | 0.7352941 | 0.54347825 | 0.36764705 | 0.32051283 | 0.5319149 | 0.2027027 | 0.44871795 | 0.6 | 0.75 | 0.3846154 | 0.2366864 | 0.51724136 | 0.5 | 0.39215687 | 0.6111111 | 0.5 | 0.3409091 | 0.3846154 | 0.54545456 | 0.5952381 | 0.41666666 | 0.63829786 | 0.3960396 | 0.5 | 0.22012578 | 0.26785713 | 0.625 |
| 5 | f1 | 0.44230735 | 0.10848757 | 0.5 | 0.4 | 0.3478261 | 0.71428573 | 0.5263158 | 0.3846154 | 0.33333334 | 0.5 | 0.20689656 | 0.42424244 | 0.54545456 | 0.54545456 | 0.42857143 | 0.29090908 | 0.48 | 0.5 | 0.4848485 | 0.6111111 | 0.5 | 0.38709676 | 0.3448276 | 0.48 | 0.41666666 | 0.42424244 | 0.46153846 | 0.45714286 | 0.5 | 0.27450982 | 0.26086956 | 0.53846157 |
| 6 | f2 | 0.43553632 | 0.1088245 | 0.47945204 | 0.30487806 | 0.32258064 | 0.6944444 | 0.5102041 | 0.4032258 | 0.3472222 | 0.4716981 | 0.2112676 | 0.40229884 | 0.5 | 0.42857143 | 0.48387095 | 0.3773585 | 0.4477612 | 0.5 | 0.63492066 | 0.6111111 | 0.5 | 0.4477612 | 0.3125 | 0.42857143 | 0.32051283 | 0.43209878 | 0.36144578 | 0.5405405 | 0.5 | 0.36458334 | 0.2542373 | 0.47297296 |
| 7 | lift_top_group | 7.742491 | 5.427792 | 17.333334 | 13.684211 | 0.0 | 7.878788 | 8.666667 | 7.2222223 | 6.1904764 | 15.757576 | 0.0 | 9.62963 | 9.122807 | 16.25 | 5.098039 | 0.0 | 6.1904764 | 16.25 | 0.0 | 9.592592 | 6.1666665 | 7.1944447 | 0.0 | 5.7555556 | 14.388889 | 5.3958335 | 9.087719 | 6.6410255 | 5.3958335 | 0.0 | 7.1944447 | 16.1875 |
| 8 | logloss | 0.18657228 | 0.026996216 | 0.16697098 | 0.2198855 | 0.17833343 | 0.19334903 | 0.21199366 | 0.16617969 | 0.18532035 | 0.13476269 | 0.20793843 | 0.21842158 | 0.19631739 | 0.18239409 | 0.20292929 | 0.22762395 | 0.16472258 | 0.17093508 | 0.12614858 | 0.16611779 | 0.16528562 | 0.16127771 | 0.2214227 | 0.17972866 | 0.22421187 | 0.19486956 | 0.22732756 | 0.15378179 | 0.19079727 | 0.20977211 | 0.1766598 | 0.17168997 |
| 9 | max_per_class_error | 0.55886775 | 0.13033332 | 0.53333336 | 0.7368421 | 0.6923077 | 0.3181818 | 0.5 | 0.5833333 | 0.64285713 | 0.54545456 | 0.78571427 | 0.6111111 | 0.5263158 | 0.625 | 0.47058824 | 0.5294118 | 0.5714286 | 0.5 | 0.2 | 0.3888889 | 0.5 | 0.5 | 0.7058824 | 0.6 | 0.7222222 | 0.5625 | 0.68421054 | 0.3846154 | 0.5 | 0.53333336 | 0.75 | 0.5625 |
| 10 | mcc | 0.42396042 | 0.11909741 | 0.47304726 | 0.4489592 | 0.321182 | 0.69016933 | 0.48987296 | 0.3535887 | 0.29344437 | 0.48282403 | 0.16021922 | 0.38739952 | 0.52226615 | 0.60019684 | 0.38872033 | 0.24291441 | 0.45778096 | 0.46721312 | 0.5010373 | 0.58206546 | 0.47142857 | 0.36067995 | 0.31243056 | 0.46507916 | 0.4625621 | 0.3853074 | 0.5010284 | 0.43737778 | 0.46707818 | 0.23484655 | 0.22682892 | 0.5312636 |
| 11 | mean_per_class_accuracy | 0.7027655 | 0.062458567 | 0.7210884 | 0.62950426 | 0.6417004 | 0.8304049 | 0.73333335 | 0.69018817 | 0.6562137 | 0.7192406 | 0.5827526 | 0.6779155 | 0.7264687 | 0.6875 | 0.73178405 | 0.67356575 | 0.70412314 | 0.7336066 | 0.86987954 | 0.79103273 | 0.73571426 | 0.7236842 | 0.632596 | 0.6918033 | 0.6368142 | 0.6981739 | 0.6558114 | 0.77923703 | 0.7335391 | 0.6739071 | 0.60880566 | 0.71257716 |
| 12 | mean_per_class_error | 0.29723448 | 0.062458567 | 0.27891156 | 0.37049574 | 0.35829958 | 0.1695951 | 0.26666668 | 0.30981183 | 0.3437863 | 0.2807594 | 0.41724738 | 0.3220845 | 0.27353135 | 0.3125 | 0.26821592 | 0.32643428 | 0.2958769 | 0.26639345 | 0.13012049 | 0.20896727 | 0.2642857 | 0.27631578 | 0.36740398 | 0.30819672 | 0.3631858 | 0.30182612 | 0.3441886 | 0.22076298 | 0.2664609 | 0.3260929 | 0.39119434 | 0.28742284 |
| 13 | mse | 0.048134618 | 0.0077235596 | 0.042385206 | 0.05768095 | 0.04577628 | 0.05566392 | 0.057245184 | 0.041172575 | 0.046906468 | 0.032283872 | 0.050813098 | 0.05706621 | 0.05317391 | 0.045463476 | 0.053945348 | 0.059274074 | 0.041707434 | 0.04442821 | 0.03129854 | 0.04487765 | 0.041792255 | 0.040929314 | 0.0583724 | 0.04580764 | 0.056718938 | 0.050193656 | 0.059246026 | 0.04041858 | 0.048819367 | 0.053397037 | 0.043386403 | 0.04379452 |
| 14 | null_deviance | 118.00269 | 15.675512 | 114.7255 | 136.7752 | 103.75808 | 153.41394 | 142.31174 | 98.28862 | 109.23703 | 92.82863 | 109.23703 | 131.24835 | 136.7752 | 120.223526 | 125.73113 | 125.73113 | 109.23703 | 120.223526 | 87.25086 | 131.12575 | 109.11213 | 98.16257 | 125.607956 | 114.60118 | 131.12575 | 120.09978 | 136.65318 | 103.63261 | 120.09978 | 114.60118 | 98.16257 | 120.09978 |
| 15 | pr_auc | 0.31300333 | 0.13261646 | 0.46633774 | 0.40207747 | 0.17097145 | 0.5481929 | 0.42493495 | 0.17691788 | 0.24908042 | 0.4225342 | 0.082122326 | 0.28827175 | 0.4293986 | 0.5072212 | 0.230104 | 0.1515979 | 0.30483416 | 0.44775507 | 0.21951866 | 0.5489945 | 0.31713557 | 0.19645564 | 0.18920143 | 0.29283372 | 0.35643643 | 0.28245872 | 0.32373628 | 0.26624346 | 0.2597664 | 0.13504013 | 0.17177226 | 0.52815443 |
| 16 | precision | 0.5050832 | 0.20756994 | 0.53846157 | 0.8333333 | 0.4 | 0.75 | 0.5555556 | 0.35714287 | 0.3125 | 0.5555556 | 0.2 | 0.46666667 | 0.64285713 | 1.0 | 0.36 | 0.21052632 | 0.54545456 | 0.5 | 0.3478261 | 0.6111111 | 0.5 | 0.31578946 | 0.41666666 | 0.6 | 0.8333333 | 0.4117647 | 0.85714287 | 0.36363637 | 0.5 | 0.19444445 | 0.27272728 | 0.7 |
| 17 | r2 | 0.14128372 | 0.08096795 | 0.22034292 | 0.14845331 | 0.036288824 | 0.28134432 | 0.19379699 | 0.064762756 | 0.079303965 | 0.20321661 | 0.0026232707 | 0.11439947 | 0.21499096 | 0.21277381 | 0.11723419 | 0.03003452 | 0.18135233 | 0.23070003 | 0.1568123 | 0.30603102 | 0.18266292 | 0.07369123 | 0.048206378 | 0.16043103 | 0.1229223 | 0.1339916 | 0.12844676 | 0.15218297 | 0.15770265 | 0.021331524 | 0.018082552 | 0.2443981 |
| 18 | recall | 0.44113225 | 0.13033332 | 0.46666667 | 0.2631579 | 0.30769232 | 0.6818182 | 0.5 | 0.41666666 | 0.35714287 | 0.45454547 | 0.21428572 | 0.3888889 | 0.47368422 | 0.375 | 0.5294118 | 0.47058824 | 0.42857143 | 0.5 | 0.8 | 0.6111111 | 0.5 | 0.5 | 0.29411766 | 0.4 | 0.2777778 | 0.4375 | 0.31578946 | 0.61538464 | 0.5 | 0.46666667 | 0.25 | 0.4375 |
| 19 | residual_deviance | 96.84632 | 14.030346 | 86.82491 | 114.34046 | 92.73338 | 100.5415 | 110.23671 | 86.41344 | 96.36658 | 70.0766 | 108.12798 | 113.579216 | 102.085045 | 94.844925 | 105.523224 | 118.364456 | 85.65574 | 88.88624 | 65.34497 | 86.04902 | 85.61795 | 83.541855 | 114.69696 | 93.09944 | 116.14175 | 100.94244 | 117.75568 | 79.658966 | 98.832985 | 108.66195 | 91.50977 | 88.93541 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:13:06 | 0.000 sec | 2 | .85E1 | 15.0 | 0.452179 | 0.451794 | 0.452411 | 0.010904 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:13:06 | 0.003 sec | 4 | .53E1 | 15.0 | 0.450779 | 0.450215 | 0.451074 | 0.01085 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:13:06 | 0.006 sec | 6 | .33E1 | 15.0 | 0.448578 | 0.447733 | 0.44897 | 0.010765 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:13:06 | 0.015 sec | 8 | .2E1 | 15.0 | 0.445156 | 0.443874 | 0.445694 | 0.010637 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:13:06 | 0.018 sec | 10 | .13E1 | 15.0 | 0.439994 | 0.438056 | 0.440743 | 0.010451 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:13:06 | 0.021 sec | 12 | .79E0 | 15.0 | 0.432552 | 0.42967 | 0.433574 | 0.010199 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:13:06 | 0.024 sec | 14 | .49E0 | 15.0 | 0.422622 | 0.418488 | 0.423948 | 0.009897 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:13:06 | 0.026 sec | 16 | .3E0 | 15.0 | 0.410979 | 0.405393 | 0.412553 | 0.009609 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:13:06 | 0.029 sec | 18 | .19E0 | 15.0 | 0.39952 | 0.392555 | 0.401217 | 0.009424 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:13:06 | 0.032 sec | 20 | .12E0 | 15.0 | 0.390157 | 0.382185 | 0.391914 | 0.009387 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:13:06 | 0.035 sec | 22 | .73E-1 | 15.0 | 0.38354 | 0.375096 | 0.385388 | 0.009466 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:13:06 | 0.038 sec | 24 | .45E-1 | 15.0 | 0.379245 | 0.370865 | 0.381259 | 0.009599 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:13:06 | 0.040 sec | 26 | .28E-1 | 15.0 | 0.376544 | 0.368662 | 0.378782 | 0.00974 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:13:06 | 0.043 sec | 28 | .17E-1 | 15.0 | 0.374842 | 0.367745 | 0.377132 | 0.009831 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:13:06 | 0.297 sec | 29 | None | NaN | 29.0 | 0.220095 | 0.186887 | 0.142546 | 0.782707 | 0.28214 | 7.891793 | 0.07475 | 0.217042 | 0.183794 | 0.165972 | 0.748225 | 0.314123 | 9.984615 | 0.076528 | ||||||
| 15 | 2021-07-15 20:13:06 | 0.046 sec | 30 | .11E-1 | 15.0 | 0.373774 | 0.367589 | 0.376254 | 0.00992 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:13:06 | 0.048 sec | 32 | .67E-2 | 15.0 | 0.373119 | 0.36783 | 0.376899 | 0.010292 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:13:06 | 0.051 sec | 34 | .42E-2 | 15.0 | 0.372735 | 0.368232 | 0.376623 | 0.010329 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:13:06 | 0.054 sec | 36 | .26E-2 | 15.0 | 0.372517 | 0.36865 | 0.378982 | 0.010752 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.546530 | 1.000000 | 0.273170 |
| 1 | Average_Transaction_Frequency | 0.303757 | 0.555791 | 0.151826 |
| 2 | Merchant_ID | 0.203774 | 0.372850 | 0.101851 |
| 3 | Minimum_Transaction_Amount | 0.161399 | 0.295315 | 0.080671 |
| 4 | Channel_ID | 0.158528 | 0.290063 | 0.079236 |
| 5 | Transaction_Amount | 0.133097 | 0.243530 | 0.066525 |
| 6 | Card_Type.1 | 0.131335 | 0.240307 | 0.065645 |
| 7 | Card_Type.0 | 0.129886 | 0.237656 | 0.064921 |
| 8 | Transaction_Date | 0.069874 | 0.127850 | 0.034925 |
| 9 | Maximum_Transaction_Amount | 0.049310 | 0.090223 | 0.024646 |
| 10 | Day | 0.041625 | 0.076162 | 0.020805 |
| 11 | Month | 0.029056 | 0.053165 | 0.014523 |
| 12 | Average_Transaction_Amount | 0.026434 | 0.048368 | 0.013213 |
| 13 | City_ID | 0.016090 | 0.029441 | 0.008042 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201310 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01075 ) | nlambda = 30, lambda.max = 8.4682, lambda.min = 0.01075, lambda.1s... | 14 | 14 | 30 | automl_training_py_345_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.048384372418263634 RMSE: 0.21996447990133233 LogLoss: 0.18721294111653794 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938266 Residual deviance: 2915.2799190667306 AIC: 2945.2799190667306 AUC: 0.7722557421928835 AUCPR: 0.27998644827992025 Gini: 0.544511484385767 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.11798608429158425:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6963.0 | 355.0 | 0.0485 | (355.0/7318.0) |
| 1 | 1 | 259.0 | 209.0 | 0.5534 | (259.0/468.0) |
| 2 | Total | 7222.0 | 564.0 | 0.0789 | (614.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.117986 | 0.405039 | 192.0 |
| 1 | max f2 | 0.068674 | 0.435621 | 224.0 |
| 2 | max f0point5 | 0.307254 | 0.413516 | 120.0 |
| 3 | max accuracy | 0.427845 | 0.940277 | 41.0 |
| 4 | max precision | 0.543510 | 0.583333 | 11.0 |
| 5 | max recall | 0.019668 | 1.000000 | 380.0 |
| 6 | max specificity | 0.802173 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.117986 | 0.365015 | 192.0 |
| 8 | max min_per_class_accuracy | 0.042130 | 0.687483 | 287.0 |
| 9 | max mean_per_class_accuracy | 0.058340 | 0.709769 | 238.0 |
| 10 | max tns | 0.802173 | 7317.000000 | 0.0 |
| 11 | max fns | 0.802173 | 468.000000 | 0.0 |
| 12 | max fps | 0.001238 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019668 | 468.000000 | 380.0 |
| 14 | max tnr | 0.802173 | 0.999863 | 0.0 |
| 15 | max fnr | 0.802173 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001238 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019668 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.419038 | 8.318376 | 8.318376 | 0.500000 | 0.489351 | 0.500000 | 0.489351 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.384862 | 7.891793 | 8.105084 | 0.474359 | 0.399629 | 0.487179 | 0.444490 | 0.079060 | 0.162393 | 689.179268 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.360070 | 6.612043 | 7.607404 | 0.397436 | 0.371862 | 0.457265 | 0.420281 | 0.066239 | 0.228632 | 561.204252 | 660.740375 | 0.211278 |
| 3 | 4 | 0.040072 | 0.340101 | 7.251918 | 7.518532 | 0.435897 | 0.350131 | 0.451923 | 0.402743 | 0.072650 | 0.301282 | 625.191760 | 651.853222 | 0.277915 |
| 4 | 5 | 0.050090 | 0.316907 | 5.758876 | 7.166601 | 0.346154 | 0.329054 | 0.430769 | 0.388005 | 0.057692 | 0.358974 | 475.887574 | 616.660092 | 0.328638 |
| 5 | 6 | 0.100051 | 0.064219 | 2.566080 | 4.869293 | 0.154242 | 0.154759 | 0.292683 | 0.271532 | 0.128205 | 0.487179 | 156.608002 | 386.929331 | 0.411886 |
| 6 | 7 | 0.150013 | 0.052037 | 0.983664 | 3.575192 | 0.059126 | 0.056680 | 0.214897 | 0.199976 | 0.049145 | 0.536325 | -1.633599 | 257.519245 | 0.411017 |
| 7 | 8 | 0.200103 | 0.047720 | 0.895825 | 2.904491 | 0.053846 | 0.049721 | 0.174583 | 0.162364 | 0.044872 | 0.581197 | -10.417488 | 190.449075 | 0.405466 |
| 8 | 9 | 0.300026 | 0.043217 | 0.876744 | 2.229154 | 0.052699 | 0.045231 | 0.133990 | 0.123353 | 0.087607 | 0.668803 | -12.325599 | 122.915386 | 0.392362 |
| 9 | 10 | 0.400077 | 0.040208 | 0.683410 | 1.842594 | 0.041078 | 0.041628 | 0.110754 | 0.102915 | 0.068376 | 0.737179 | -31.659041 | 84.259374 | 0.358661 |
| 10 | 11 | 0.500000 | 0.037741 | 0.705672 | 1.615385 | 0.042416 | 0.038945 | 0.097097 | 0.090131 | 0.070513 | 0.807692 | -29.432799 | 61.538462 | 0.327370 |
| 11 | 12 | 0.600051 | 0.035405 | 0.640696 | 1.452867 | 0.038511 | 0.036573 | 0.087329 | 0.081201 | 0.064103 | 0.871795 | -35.930351 | 45.286705 | 0.289122 |
| 12 | 13 | 0.699974 | 0.033007 | 0.427680 | 1.306519 | 0.025707 | 0.034228 | 0.078532 | 0.074495 | 0.042735 | 0.914530 | -57.232000 | 30.651925 | 0.228277 |
| 13 | 14 | 0.800026 | 0.030049 | 0.320348 | 1.183189 | 0.019255 | 0.031618 | 0.071119 | 0.069133 | 0.032051 | 0.946581 | -67.965176 | 18.318850 | 0.155928 |
| 14 | 15 | 0.899949 | 0.025477 | 0.277992 | 1.082683 | 0.016710 | 0.028023 | 0.065078 | 0.064569 | 0.027778 | 0.974359 | -72.200800 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.000975 | 0.256279 | 1.000000 | 0.015404 | 0.019985 | 0.060108 | 0.060108 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04730743106389137 RMSE: 0.2175027150724592 LogLoss: 0.18277914202737372 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311447 Residual deviance: 711.7419790545933 AIC: 741.7419790545933 AUC: 0.7879407781047125 AUCPR: 0.3249284451868777 Gini: 0.575881556209425 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.25719033273899006:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1750.0 | 80.0 | 0.0437 | (80.0/1830.0) |
| 1 | 1 | 63.0 | 54.0 | 0.5385 | (63.0/117.0) |
| 2 | Total | 1813.0 | 134.0 | 0.0734 | (143.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.257190 | 0.430279 | 110.0 |
| 1 | max f2 | 0.064626 | 0.467359 | 167.0 |
| 2 | max f0point5 | 0.356334 | 0.415648 | 60.0 |
| 3 | max accuracy | 0.477028 | 0.942476 | 8.0 |
| 4 | max precision | 0.812976 | 1.000000 | 0.0 |
| 5 | max recall | 0.020804 | 1.000000 | 369.0 |
| 6 | max specificity | 0.812976 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.257190 | 0.392247 | 110.0 |
| 8 | max min_per_class_accuracy | 0.043074 | 0.709402 | 237.0 |
| 9 | max mean_per_class_accuracy | 0.050843 | 0.730566 | 201.0 |
| 10 | max tns | 0.812976 | 1830.000000 | 0.0 |
| 11 | max fns | 0.812976 | 116.000000 | 0.0 |
| 12 | max fps | 0.000937 | 1830.000000 | 399.0 |
| 13 | max tps | 0.020804 | 117.000000 | 369.0 |
| 14 | max tnr | 0.812976 | 1.000000 | 0.0 |
| 15 | max fnr | 0.812976 | 0.991453 | 0.0 |
| 16 | max fpr | 0.000937 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020804 | 1.000000 | 369.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.18 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.425197 | 8.320513 | 8.320513 | 0.500000 | 0.492806 | 0.500000 | 0.492806 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.387357 | 8.758435 | 8.533859 | 0.526316 | 0.406099 | 0.512821 | 0.450564 | 0.085470 | 0.170940 | 775.843455 | 753.385930 | 0.160558 |
| 2 | 3 | 0.030303 | 0.366011 | 7.488462 | 8.179487 | 0.450000 | 0.376568 | 0.491525 | 0.425481 | 0.076923 | 0.247863 | 648.846154 | 717.948718 | 0.231470 |
| 3 | 4 | 0.040062 | 0.349746 | 5.255061 | 7.467127 | 0.315789 | 0.357701 | 0.448718 | 0.408970 | 0.051282 | 0.299145 | 425.506073 | 646.712689 | 0.275648 |
| 4 | 5 | 0.050334 | 0.327104 | 4.160256 | 6.792255 | 0.250000 | 0.339498 | 0.408163 | 0.394792 | 0.042735 | 0.341880 | 316.025641 | 579.225536 | 0.310186 |
| 5 | 6 | 0.100154 | 0.068626 | 3.602696 | 5.205654 | 0.216495 | 0.180719 | 0.312821 | 0.288305 | 0.179487 | 0.521368 | 260.269627 | 420.565417 | 0.448143 |
| 6 | 7 | 0.149974 | 0.052364 | 1.029342 | 3.818318 | 0.061856 | 0.058726 | 0.229452 | 0.212041 | 0.051282 | 0.572650 | 2.934179 | 281.831753 | 0.449699 |
| 7 | 8 | 0.200308 | 0.047909 | 1.018838 | 3.114859 | 0.061224 | 0.049773 | 0.187179 | 0.171266 | 0.051282 | 0.623932 | 1.883830 | 211.485865 | 0.450708 |
| 8 | 9 | 0.299949 | 0.043115 | 0.772006 | 2.336582 | 0.046392 | 0.045265 | 0.140411 | 0.129409 | 0.076923 | 0.700855 | -22.799366 | 133.658237 | 0.426538 |
| 9 | 10 | 0.400103 | 0.039757 | 0.341354 | 1.837135 | 0.020513 | 0.041431 | 0.110398 | 0.107386 | 0.034188 | 0.735043 | -65.864563 | 83.713505 | 0.356354 |
| 10 | 11 | 0.500257 | 0.037357 | 0.853386 | 1.640183 | 0.051282 | 0.038589 | 0.098563 | 0.093613 | 0.085470 | 0.820513 | -14.661407 | 64.018323 | 0.340731 |
| 11 | 12 | 0.599897 | 0.035128 | 0.686228 | 1.481735 | 0.041237 | 0.036242 | 0.089041 | 0.084084 | 0.068376 | 0.888889 | -31.377214 | 48.173516 | 0.307468 |
| 12 | 13 | 0.700051 | 0.032755 | 0.256016 | 1.306375 | 0.015385 | 0.033973 | 0.078503 | 0.076915 | 0.025641 | 0.914530 | -74.398422 | 30.637545 | 0.228191 |
| 13 | 14 | 0.799692 | 0.030048 | 0.171557 | 1.164979 | 0.010309 | 0.031449 | 0.070006 | 0.071250 | 0.017094 | 0.931624 | -82.844303 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.025339 | 0.426693 | 1.082806 | 0.025641 | 0.027806 | 0.065068 | 0.066414 | 0.042735 | 0.974359 | -57.330703 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.000937 | 0.256016 | 1.000000 | 0.015385 | 0.020157 | 0.060092 | 0.061781 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048694590135074316 RMSE: 0.22066850734772805 LogLoss: 0.18863560645635324 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.972297633706 Residual deviance: 2937.433663738332 AIC: 2967.433663738332 AUC: 0.7587227840029152 AUCPR: 0.26772085207707885 Gini: 0.5174455680058303 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.1067201384741719:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6954.0 | 364.0 | 0.0497 | (364.0/7318.0) |
| 1 | 1 | 258.0 | 210.0 | 0.5513 | (258.0/468.0) |
| 2 | Total | 7212.0 | 574.0 | 0.0799 | (622.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.106720 | 0.403071 | 193.0 |
| 1 | max f2 | 0.068200 | 0.432360 | 222.0 |
| 2 | max f0point5 | 0.339961 | 0.409836 | 98.0 |
| 3 | max accuracy | 0.616190 | 0.940021 | 5.0 |
| 4 | max precision | 0.616190 | 0.571429 | 5.0 |
| 5 | max recall | 0.020013 | 1.000000 | 379.0 |
| 6 | max specificity | 0.826959 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.106720 | 0.362897 | 193.0 |
| 8 | max min_per_class_accuracy | 0.042094 | 0.679487 | 285.0 |
| 9 | max mean_per_class_accuracy | 0.058787 | 0.707247 | 236.0 |
| 10 | max tns | 0.826959 | 7317.000000 | 0.0 |
| 11 | max fns | 0.826959 | 468.000000 | 0.0 |
| 12 | max fps | 0.001269 | 7318.000000 | 399.0 |
| 13 | max tps | 0.020013 | 468.000000 | 379.0 |
| 14 | max tnr | 0.826959 | 0.999863 | 0.0 |
| 15 | max fnr | 0.826959 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001269 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020013 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.418583 | 8.318376 | 8.318376 | 0.500000 | 0.490960 | 0.500000 | 0.490960 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.383300 | 6.825334 | 7.571855 | 0.410256 | 0.398772 | 0.455128 | 0.444866 | 0.068376 | 0.151709 | 582.533421 | 657.185514 | 0.140094 |
| 2 | 3 | 0.030054 | 0.360155 | 7.038626 | 7.394112 | 0.423077 | 0.371119 | 0.444444 | 0.420284 | 0.070513 | 0.222222 | 603.862590 | 639.411206 | 0.204458 |
| 3 | 4 | 0.040072 | 0.339254 | 7.891793 | 7.518532 | 0.474359 | 0.348820 | 0.451923 | 0.402418 | 0.079060 | 0.301282 | 689.179268 | 651.853222 | 0.277915 |
| 4 | 5 | 0.050090 | 0.315820 | 4.905709 | 6.995968 | 0.294872 | 0.328621 | 0.420513 | 0.387658 | 0.049145 | 0.350427 | 390.570896 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.064056 | 2.737152 | 4.869293 | 0.164524 | 0.154487 | 0.292683 | 0.271222 | 0.136752 | 0.487179 | 173.715202 | 386.929331 | 0.411886 |
| 6 | 7 | 0.150013 | 0.052090 | 0.855360 | 3.532461 | 0.051414 | 0.056642 | 0.212329 | 0.199757 | 0.042735 | 0.529915 | -14.463999 | 253.246107 | 0.404197 |
| 7 | 8 | 0.200103 | 0.047783 | 0.639875 | 2.808386 | 0.038462 | 0.049807 | 0.168806 | 0.162221 | 0.032051 | 0.561966 | -36.012492 | 180.838627 | 0.385005 |
| 8 | 9 | 0.300026 | 0.043279 | 0.898128 | 2.172179 | 0.053985 | 0.045270 | 0.130565 | 0.123271 | 0.089744 | 0.651709 | -10.187199 | 117.217868 | 0.374175 |
| 9 | 10 | 0.400077 | 0.040277 | 0.662053 | 1.794526 | 0.039795 | 0.041686 | 0.107865 | 0.102868 | 0.066239 | 0.717949 | -33.794696 | 79.452607 | 0.338200 |
| 10 | 11 | 0.500000 | 0.037772 | 0.662904 | 1.568376 | 0.039846 | 0.039008 | 0.094272 | 0.090106 | 0.066239 | 0.784188 | -33.709599 | 56.837607 | 0.302362 |
| 11 | 12 | 0.600051 | 0.035400 | 0.640696 | 1.413697 | 0.038511 | 0.036580 | 0.084974 | 0.081181 | 0.064103 | 0.848291 | -35.930351 | 41.369662 | 0.264115 |
| 12 | 13 | 0.699974 | 0.033086 | 0.491832 | 1.282098 | 0.029563 | 0.034237 | 0.077064 | 0.074480 | 0.049145 | 0.897436 | -50.816800 | 28.209833 | 0.210090 |
| 13 | 14 | 0.800026 | 0.030169 | 0.341705 | 1.164493 | 0.020539 | 0.031707 | 0.069995 | 0.069130 | 0.034188 | 0.931624 | -65.829521 | 16.449252 | 0.140014 |
| 14 | 15 | 0.899949 | 0.025561 | 0.406296 | 1.080309 | 0.024422 | 0.028093 | 0.064935 | 0.064574 | 0.040598 | 0.972222 | -59.370400 | 8.030858 | 0.076896 |
| 15 | 16 | 1.000000 | 0.000987 | 0.277635 | 1.000000 | 0.016688 | 0.020131 | 0.060108 | 0.060127 | 0.027778 | 1.000000 | -72.236486 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9337135 | 0.021645065 | 0.95 | 0.89615387 | 0.9576923 | 0.95384616 | 0.9653846 | 0.9269231 | 0.9346154 | 0.9576923 | 0.95 | 0.95 | 0.91923076 | 0.93846154 | 0.93846154 | 0.95384616 | 0.93846154 | 0.9153846 | 0.9459459 | 0.93822396 | 0.8880309 | 0.8880309 | 0.90733594 | 0.95366794 | 0.96138996 | 0.93436295 | 0.9189189 | 0.9189189 | 0.93436295 | 0.9111969 | 0.9498069 | 0.9150579 |
| 1 | auc | 0.7616094 | 0.07724876 | 0.8720025 | 0.62911564 | 0.78998655 | 0.73793715 | 0.8016822 | 0.74590164 | 0.84481174 | 0.5892662 | 0.7705156 | 0.67579406 | 0.72145844 | 0.74316144 | 0.8617768 | 0.7764 | 0.70463675 | 0.6898053 | 0.7795782 | 0.8012909 | 0.77623934 | 0.75504386 | 0.74069625 | 0.7006173 | 0.8471074 | 0.7239067 | 0.8257171 | 0.9192387 | 0.816035 | 0.60504204 | 0.82073045 | 0.7827869 |
| 2 | err | 0.066286504 | 0.021645065 | 0.05 | 0.103846155 | 0.042307694 | 0.046153847 | 0.034615386 | 0.073076926 | 0.06538462 | 0.042307694 | 0.05 | 0.05 | 0.08076923 | 0.06153846 | 0.06153846 | 0.046153847 | 0.06153846 | 0.08461539 | 0.054054055 | 0.06177606 | 0.11196911 | 0.11196911 | 0.09266409 | 0.046332046 | 0.038610037 | 0.06563707 | 0.08108108 | 0.08108108 | 0.06563707 | 0.08880309 | 0.05019305 | 0.08494209 |
| 3 | err_count | 17.2 | 5.6041856 | 13.0 | 27.0 | 11.0 | 12.0 | 9.0 | 19.0 | 17.0 | 11.0 | 13.0 | 13.0 | 21.0 | 16.0 | 16.0 | 12.0 | 16.0 | 22.0 | 14.0 | 16.0 | 29.0 | 29.0 | 24.0 | 12.0 | 10.0 | 17.0 | 21.0 | 21.0 | 17.0 | 23.0 | 13.0 | 22.0 |
| 4 | f0point5 | 0.45280942 | 0.109657064 | 0.4918033 | 0.27027026 | 0.53571427 | 0.33333334 | 0.5102041 | 0.39473686 | 0.5319149 | 0.37037036 | 0.6164383 | 0.39215687 | 0.4950495 | 0.4716981 | 0.5445545 | 0.45454547 | 0.27272728 | 0.3125 | 0.546875 | 0.5405405 | 0.44827586 | 0.32520324 | 0.37037036 | 0.625 | 0.71428573 | 0.42682928 | 0.42857143 | 0.44354838 | 0.42682928 | 0.32786885 | 0.5882353 | 0.37383178 |
| 5 | f1 | 0.4439309 | 0.10607545 | 0.48 | 0.30769232 | 0.5217391 | 0.33333334 | 0.5263158 | 0.38709676 | 0.5405405 | 0.26666668 | 0.58064514 | 0.3809524 | 0.4878049 | 0.3846154 | 0.57894737 | 0.5 | 0.27272728 | 0.3125 | 0.5 | 0.5 | 0.47272727 | 0.35555556 | 0.33333334 | 0.5 | 0.6875 | 0.4516129 | 0.46153846 | 0.5116279 | 0.4516129 | 0.2580645 | 0.55172414 | 0.42105263 |
| 6 | f2 | 0.4436562 | 0.11648918 | 0.46875 | 0.35714287 | 0.5084746 | 0.33333334 | 0.54347825 | 0.37974682 | 0.5494506 | 0.20833333 | 0.5487805 | 0.37037036 | 0.48076922 | 0.32467532 | 0.6179775 | 0.5555556 | 0.27272728 | 0.3125 | 0.46052632 | 0.4651163 | 0.5 | 0.39215687 | 0.3030303 | 0.41666666 | 0.6626506 | 0.47945204 | 0.5 | 0.6043956 | 0.47945204 | 0.21276596 | 0.5194805 | 0.48192772 |
| 7 | lift_top_group | 8.404761 | 4.877896 | 13.333333 | 0.0 | 14.444445 | 9.62963 | 19.25926 | 5.4166665 | 14.444445 | 7.878788 | 10.196078 | 7.878788 | 8.253968 | 10.196078 | 5.098039 | 8.666667 | 0.0 | 0.0 | 10.791667 | 4.796296 | 6.9066668 | 4.5438595 | 8.222222 | 10.791667 | 15.235294 | 12.333333 | 5.0784316 | 10.791667 | 12.333333 | 4.111111 | 0.0 | 11.511111 |
| 8 | logloss | 0.18690424 | 0.0392664 | 0.14933948 | 0.20352997 | 0.14265531 | 0.14032707 | 0.117355585 | 0.2009143 | 0.18110858 | 0.17285751 | 0.17779875 | 0.15552962 | 0.23324892 | 0.2069407 | 0.16872402 | 0.12869464 | 0.16297443 | 0.21217288 | 0.18888901 | 0.20247997 | 0.2799388 | 0.22999364 | 0.25910357 | 0.19356813 | 0.15800796 | 0.1745882 | 0.18850823 | 0.17275645 | 0.17008871 | 0.27358904 | 0.17857067 | 0.18287304 |
| 9 | max_per_class_error | 0.5523355 | 0.13008046 | 0.53846157 | 0.6 | 0.5 | 0.6666667 | 0.44444445 | 0.625 | 0.44444445 | 0.8181818 | 0.47058824 | 0.6363636 | 0.52380955 | 0.7058824 | 0.3529412 | 0.4 | 0.72727275 | 0.6875 | 0.5625 | 0.5555556 | 0.48 | 0.57894737 | 0.71428573 | 0.625 | 0.3529412 | 0.5 | 0.47058824 | 0.3125 | 0.5 | 0.8095238 | 0.5 | 0.46666667 |
| 10 | mcc | 0.41600734 | 0.10896479 | 0.4541826 | 0.26302132 | 0.5001678 | 0.30942896 | 0.5091635 | 0.34849003 | 0.5055856 | 0.28422415 | 0.5572801 | 0.3553996 | 0.44414756 | 0.37547213 | 0.54969865 | 0.4839347 | 0.24059875 | 0.26741803 | 0.47750816 | 0.47182348 | 0.41280735 | 0.30023405 | 0.28968942 | 0.51035756 | 0.6684936 | 0.4192773 | 0.4225452 | 0.4897649 | 0.4192773 | 0.23414357 | 0.52861995 | 0.38746446 |
| 11 | mean_per_class_accuracy | 0.70614314 | 0.063050486 | 0.71862346 | 0.6632653 | 0.73991936 | 0.65471447 | 0.7678176 | 0.66905737 | 0.75918275 | 0.586893 | 0.75441784 | 0.66977 | 0.7171747 | 0.6388284 | 0.8029533 | 0.784 | 0.6202994 | 0.633709 | 0.70846194 | 0.7097741 | 0.7236752 | 0.6730263 | 0.6239496 | 0.6833848 | 0.81526494 | 0.72959185 | 0.7378464 | 0.8108282 | 0.72959185 | 0.5826331 | 0.73971194 | 0.73592895 |
| 12 | mean_per_class_error | 0.29385683 | 0.063050486 | 0.2813765 | 0.33673468 | 0.26008064 | 0.34528553 | 0.23218238 | 0.33094263 | 0.24081726 | 0.41310698 | 0.24558218 | 0.33023 | 0.28282526 | 0.36117163 | 0.19704673 | 0.216 | 0.37970063 | 0.366291 | 0.29153806 | 0.29022592 | 0.27632478 | 0.32697368 | 0.3760504 | 0.31661522 | 0.18473504 | 0.27040815 | 0.26215363 | 0.1891718 | 0.27040815 | 0.41736695 | 0.26028806 | 0.26407105 |
| 13 | mse | 0.04823078 | 0.011826388 | 0.038198292 | 0.051721647 | 0.035053544 | 0.032453645 | 0.0263812 | 0.051784165 | 0.047812343 | 0.040308587 | 0.045242645 | 0.037439507 | 0.062438615 | 0.053866938 | 0.04568314 | 0.03035588 | 0.039182186 | 0.054334417 | 0.04908125 | 0.053936347 | 0.07722572 | 0.061677877 | 0.06891137 | 0.049697362 | 0.040543742 | 0.044253554 | 0.050761595 | 0.04778804 | 0.04308604 | 0.07210979 | 0.048201572 | 0.047392294 |
| 14 | null_deviance | 118.03241 | 20.840866 | 103.75808 | 114.7255 | 98.28862 | 81.936905 | 81.936905 | 120.223526 | 131.24835 | 92.82863 | 125.73113 | 92.82863 | 147.85797 | 125.73113 | 125.73113 | 87.37807 | 92.82863 | 120.223526 | 120.09978 | 131.12575 | 170.02197 | 136.65318 | 147.73709 | 120.09978 | 125.607956 | 109.11213 | 125.607956 | 120.09978 | 109.11213 | 147.73709 | 120.09978 | 114.60118 |
| 15 | pr_auc | 0.30627128 | 0.121309154 | 0.3720471 | 0.13697802 | 0.39411128 | 0.13534173 | 0.43478897 | 0.20807049 | 0.4936711 | 0.107807405 | 0.45929125 | 0.16212913 | 0.3386819 | 0.29830664 | 0.39199713 | 0.31429163 | 0.11181334 | 0.14787021 | 0.33047813 | 0.2847075 | 0.38515577 | 0.28331986 | 0.28870466 | 0.36342308 | 0.62830645 | 0.26378348 | 0.3149578 | 0.36340642 | 0.36136198 | 0.17596161 | 0.3189272 | 0.31844762 |
| 16 | precision | 0.4657828 | 0.1250078 | 0.5 | 0.25 | 0.54545456 | 0.33333334 | 0.5 | 0.4 | 0.5263158 | 0.5 | 0.64285713 | 0.4 | 0.5 | 0.5555556 | 0.52380955 | 0.42857143 | 0.27272728 | 0.3125 | 0.5833333 | 0.5714286 | 0.43333334 | 0.30769232 | 0.4 | 0.75 | 0.73333335 | 0.4117647 | 0.4090909 | 0.4074074 | 0.4117647 | 0.4 | 0.61538464 | 0.3478261 |
| 17 | r2 | 0.14151415 | 0.078854546 | 0.1958254 | 0.04860317 | 0.20375691 | 0.028832883 | 0.2105493 | 0.10332746 | 0.25800866 | 0.005162247 | 0.25964588 | 0.07597277 | 0.15902562 | 0.11851731 | 0.25243756 | 0.17917703 | 0.03296252 | 0.059168372 | 0.15318432 | 0.1659511 | 0.11446521 | 0.09267246 | 0.075101234 | 0.14255431 | 0.3389123 | 0.13452688 | 0.17230469 | 0.17549652 | 0.15736015 | 0.032173492 | 0.1683617 | 0.13138731 |
| 18 | recall | 0.4476645 | 0.13008046 | 0.46153846 | 0.4 | 0.5 | 0.33333334 | 0.5555556 | 0.375 | 0.5555556 | 0.18181819 | 0.5294118 | 0.36363637 | 0.47619048 | 0.29411766 | 0.64705884 | 0.6 | 0.27272728 | 0.3125 | 0.4375 | 0.44444445 | 0.52 | 0.42105263 | 0.2857143 | 0.375 | 0.64705884 | 0.5 | 0.5294118 | 0.6875 | 0.5 | 0.1904762 | 0.5 | 0.53333336 |
| 19 | residual_deviance | 97.00001 | 20.297655 | 77.65653 | 105.83559 | 74.18076 | 72.97008 | 61.024902 | 104.47543 | 94.17646 | 89.88591 | 92.45535 | 80.875404 | 121.28944 | 107.60916 | 87.73649 | 66.92121 | 84.746704 | 110.329895 | 97.844505 | 104.88463 | 145.00829 | 119.13671 | 134.21565 | 100.268295 | 81.84812 | 90.43669 | 97.64726 | 89.48784 | 88.10596 | 141.71913 | 92.4996 | 94.72823 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:13:16 | 0.000 sec | 2 | .85E1 | 15.0 | 0.452167 | 0.451821 | 0.452604 | 0.014672 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:13:16 | 0.004 sec | 4 | .53E1 | 15.0 | 0.45076 | 0.450259 | 0.451258 | 0.014634 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:13:16 | 0.007 sec | 6 | .33E1 | 15.0 | 0.448549 | 0.447803 | 0.449142 | 0.014576 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:13:16 | 0.010 sec | 8 | .2E1 | 15.0 | 0.445113 | 0.443983 | 0.445848 | 0.014489 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:13:16 | 0.013 sec | 10 | .13E1 | 15.0 | 0.439934 | 0.438223 | 0.440873 | 0.014364 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:13:16 | 0.016 sec | 12 | .78E0 | 15.0 | 0.432475 | 0.429916 | 0.433677 | 0.014199 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:13:16 | 0.019 sec | 14 | .49E0 | 15.0 | 0.422537 | 0.418826 | 0.424032 | 0.014014 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:13:16 | 0.022 sec | 16 | .3E0 | 15.0 | 0.410913 | 0.405813 | 0.41264 | 0.013858 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:13:16 | 0.026 sec | 18 | .19E0 | 15.0 | 0.399504 | 0.392979 | 0.401352 | 0.013795 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:13:16 | 0.029 sec | 20 | .12E0 | 15.0 | 0.390238 | 0.382495 | 0.392144 | 0.013845 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:13:16 | 0.032 sec | 22 | .72E-1 | 15.0 | 0.38375 | 0.375138 | 0.385752 | 0.013966 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:13:16 | 0.035 sec | 24 | .45E-1 | 15.0 | 0.379599 | 0.370491 | 0.381775 | 0.014104 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:13:16 | 0.038 sec | 26 | .28E-1 | 15.0 | 0.37703 | 0.367764 | 0.379437 | 0.014223 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:13:16 | 0.041 sec | 28 | .17E-1 | 15.0 | 0.375429 | 0.366271 | 0.378092 | 0.014315 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:13:16 | 0.283 sec | 29 | None | NaN | 29.0 | 0.219964 | 0.187213 | 0.143563 | 0.772256 | 0.279986 | 8.318376 | 0.078859 | 0.217503 | 0.182779 | 0.162424 | 0.787941 | 0.324928 | 8.320513 | 0.073446 | ||||||
| 15 | 2021-07-15 20:13:16 | 0.044 sec | 30 | .11E-1 | 15.0 | 0.374426 | 0.365558 | 0.377332 | 0.014381 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:13:16 | 0.048 sec | 32 | .67E-2 | 15.0 | 0.373803 | 0.365319 | 0.377657 | 0.014627 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:13:16 | 0.051 sec | 34 | .41E-2 | 15.0 | 0.373432 | 0.365345 | 0.379142 | 0.014892 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:13:16 | 0.054 sec | 36 | .26E-2 | 15.0 | 0.373221 | 0.365493 | 0.379978 | 0.014715 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.536121 | 1.000000 | 0.265916 |
| 1 | Average_Transaction_Frequency | 0.274541 | 0.512087 | 0.136172 |
| 2 | Channel_ID | 0.181979 | 0.339436 | 0.090262 |
| 3 | Merchant_ID | 0.171131 | 0.319202 | 0.084881 |
| 4 | Minimum_Transaction_Amount | 0.165845 | 0.309341 | 0.082259 |
| 5 | Card_Type.1 | 0.162005 | 0.302179 | 0.080354 |
| 6 | Card_Type.0 | 0.160385 | 0.299158 | 0.079551 |
| 7 | Transaction_Amount | 0.120629 | 0.225003 | 0.059832 |
| 8 | Transaction_Date | 0.069975 | 0.130520 | 0.034707 |
| 9 | Maximum_Transaction_Amount | 0.045734 | 0.085305 | 0.022684 |
| 10 | Average_Transaction_Amount | 0.043084 | 0.080362 | 0.021370 |
| 11 | Month | 0.037078 | 0.069161 | 0.018391 |
| 12 | Day | 0.026710 | 0.049822 | 0.013248 |
| 13 | City_ID | 0.020912 | 0.039006 | 0.010372 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201319 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01078 ) | nlambda = 30, lambda.max = 8.4943, lambda.min = 0.01078, lambda.1s... | 14 | 14 | 30 | automl_training_py_374_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04820631165434088 RMSE: 0.21955935792933282 LogLoss: 0.1867389072236617 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938348 Residual deviance: 2907.8982632868606 AIC: 2937.8982632868606 AUC: 0.7696367754956167 AUCPR: 0.2875905258225088 Gini: 0.5392735509912334 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2897580847115576:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7038.0 | 280.0 | 0.0383 | (280.0/7318.0) |
| 1 | 1 | 274.0 | 194.0 | 0.5855 | (274.0/468.0) |
| 2 | Total | 7312.0 | 474.0 | 0.0712 | (554.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.289758 | 0.411890 | 138.0 |
| 1 | max f2 | 0.071127 | 0.441628 | 226.0 |
| 2 | max f0point5 | 0.332543 | 0.416667 | 106.0 |
| 3 | max accuracy | 0.562624 | 0.940663 | 10.0 |
| 4 | max precision | 0.861498 | 1.000000 | 0.0 |
| 5 | max recall | 0.019835 | 1.000000 | 386.0 |
| 6 | max specificity | 0.861498 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.289758 | 0.374032 | 138.0 |
| 8 | max min_per_class_accuracy | 0.040877 | 0.688576 | 295.0 |
| 9 | max mean_per_class_accuracy | 0.059409 | 0.714888 | 241.0 |
| 10 | max tns | 0.861498 | 7318.000000 | 0.0 |
| 11 | max fns | 0.861498 | 467.000000 | 0.0 |
| 12 | max fps | 0.002589 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019835 | 468.000000 | 386.0 |
| 14 | max tnr | 0.861498 | 1.000000 | 0.0 |
| 15 | max fnr | 0.861498 | 0.997863 | 0.0 |
| 16 | max fpr | 0.002589 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019835 | 1.000000 | 386.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.412072 | 8.531668 | 8.531668 | 0.512821 | 0.495333 | 0.512821 | 0.495333 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.376252 | 7.465209 | 7.998439 | 0.448718 | 0.391837 | 0.480769 | 0.443585 | 0.074786 | 0.160256 | 646.520929 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.354383 | 6.398751 | 7.465209 | 0.384615 | 0.364817 | 0.448718 | 0.417329 | 0.064103 | 0.224359 | 539.875082 | 646.520929 | 0.206731 |
| 3 | 4 | 0.040072 | 0.337452 | 7.465209 | 7.465209 | 0.448718 | 0.344495 | 0.448718 | 0.399120 | 0.074786 | 0.299145 | 646.520929 | 646.520929 | 0.275642 |
| 4 | 5 | 0.050090 | 0.315815 | 4.905709 | 6.953309 | 0.294872 | 0.327422 | 0.417949 | 0.384781 | 0.049145 | 0.348291 | 390.570896 | 595.330923 | 0.317271 |
| 5 | 6 | 0.100051 | 0.066140 | 2.950992 | 4.954720 | 0.177378 | 0.169944 | 0.297818 | 0.277500 | 0.147436 | 0.495726 | 195.099202 | 395.471951 | 0.420979 |
| 6 | 7 | 0.150013 | 0.051229 | 0.684288 | 3.532461 | 0.041131 | 0.056473 | 0.212329 | 0.203888 | 0.034188 | 0.529915 | -31.571199 | 253.246107 | 0.404197 |
| 7 | 8 | 0.200103 | 0.046322 | 1.066458 | 2.915169 | 0.064103 | 0.048461 | 0.175225 | 0.164981 | 0.053419 | 0.583333 | 6.645847 | 191.516902 | 0.407739 |
| 8 | 9 | 0.300026 | 0.041781 | 0.769824 | 2.200666 | 0.046272 | 0.043719 | 0.132277 | 0.124595 | 0.076923 | 0.660256 | -23.017599 | 120.066627 | 0.383268 |
| 9 | 10 | 0.400077 | 0.038723 | 0.662053 | 1.815889 | 0.039795 | 0.040247 | 0.109149 | 0.103501 | 0.066239 | 0.726496 | -33.794696 | 81.588948 | 0.347294 |
| 10 | 11 | 0.500000 | 0.036408 | 0.620136 | 1.576923 | 0.037275 | 0.037559 | 0.094786 | 0.090323 | 0.061966 | 0.788462 | -37.986399 | 57.692308 | 0.306909 |
| 11 | 12 | 0.600051 | 0.034233 | 0.790192 | 1.445745 | 0.047497 | 0.035336 | 0.086901 | 0.081155 | 0.079060 | 0.867521 | -20.980766 | 44.574516 | 0.284575 |
| 12 | 13 | 0.699974 | 0.032017 | 0.363528 | 1.291256 | 0.021851 | 0.033116 | 0.077615 | 0.074297 | 0.036325 | 0.903846 | -63.647200 | 29.125618 | 0.216910 |
| 13 | 14 | 0.800026 | 0.029734 | 0.448488 | 1.185859 | 0.026958 | 0.030920 | 0.071279 | 0.068872 | 0.044872 | 0.948718 | -55.151246 | 18.585936 | 0.158201 |
| 14 | 15 | 0.899949 | 0.026308 | 0.277992 | 1.085057 | 0.016710 | 0.028211 | 0.065220 | 0.064358 | 0.027778 | 0.976496 | -72.200800 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.002274 | 0.234922 | 1.000000 | 0.014121 | 0.021882 | 0.060108 | 0.060108 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.048064560186343364 RMSE: 0.2192363112861174 LogLoss: 0.18549850523722258 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311479 Residual deviance: 722.331179393745 AIC: 752.331179393745 AUC: 0.7854584092289011 AUCPR: 0.2878336462318354 Gini: 0.5709168184578022 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.28216118238171506:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1757.0 | 73.0 | 0.0399 | (73.0/1830.0) |
| 1 | 1 | 66.0 | 51.0 | 0.5641 | (66.0/117.0) |
| 2 | Total | 1823.0 | 124.0 | 0.0714 | (139.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.282161 | 0.423237 | 107.0 |
| 1 | max f2 | 0.060444 | 0.454545 | 160.0 |
| 2 | max f0point5 | 0.344366 | 0.440806 | 61.0 |
| 3 | max accuracy | 0.420576 | 0.941448 | 21.0 |
| 4 | max precision | 0.420576 | 0.565217 | 21.0 |
| 5 | max recall | 0.022301 | 1.000000 | 371.0 |
| 6 | max specificity | 0.785927 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.282161 | 0.385405 | 107.0 |
| 8 | max min_per_class_accuracy | 0.041002 | 0.689617 | 242.0 |
| 9 | max mean_per_class_accuracy | 0.053893 | 0.724051 | 177.0 |
| 10 | max tns | 0.785927 | 1829.000000 | 0.0 |
| 11 | max fns | 0.785927 | 117.000000 | 0.0 |
| 12 | max fps | 0.002798 | 1830.000000 | 399.0 |
| 13 | max tps | 0.022301 | 117.000000 | 371.0 |
| 14 | max tnr | 0.785927 | 0.999454 | 0.0 |
| 15 | max fnr | 0.785927 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002798 | 1.000000 | 399.0 |
| 17 | max tpr | 0.022301 | 1.000000 | 371.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.93 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.422321 | 8.320513 | 8.320513 | 0.500000 | 0.484420 | 0.500000 | 0.484420 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.379015 | 7.882591 | 8.107166 | 0.473684 | 0.403027 | 0.487179 | 0.444767 | 0.076923 | 0.162393 | 688.259109 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.359012 | 9.152564 | 8.461538 | 0.550000 | 0.370040 | 0.508475 | 0.419436 | 0.094017 | 0.256410 | 815.256410 | 746.153846 | 0.240563 |
| 3 | 4 | 0.040062 | 0.333248 | 4.379217 | 7.467127 | 0.263158 | 0.346068 | 0.448718 | 0.401564 | 0.042735 | 0.299145 | 337.921727 | 646.712689 | 0.275648 |
| 4 | 5 | 0.050334 | 0.313213 | 4.992308 | 6.962062 | 0.300000 | 0.323773 | 0.418367 | 0.385688 | 0.051282 | 0.350427 | 399.230769 | 596.206175 | 0.319280 |
| 5 | 6 | 0.100154 | 0.061631 | 2.916468 | 4.949638 | 0.175258 | 0.153413 | 0.297436 | 0.270146 | 0.145299 | 0.495726 | 191.646841 | 394.963840 | 0.420863 |
| 6 | 7 | 0.149974 | 0.050275 | 1.029342 | 3.647348 | 0.061856 | 0.054612 | 0.219178 | 0.198547 | 0.051282 | 0.547009 | 2.934179 | 264.734809 | 0.422418 |
| 7 | 8 | 0.200308 | 0.046274 | 1.018838 | 2.986851 | 0.061224 | 0.048037 | 0.179487 | 0.160727 | 0.051282 | 0.598291 | 1.883830 | 198.685076 | 0.423427 |
| 8 | 9 | 0.299949 | 0.041988 | 0.772006 | 2.251098 | 0.046392 | 0.043838 | 0.135274 | 0.121897 | 0.076923 | 0.675214 | -22.799366 | 125.109765 | 0.399257 |
| 9 | 10 | 0.400103 | 0.039054 | 0.597370 | 1.837135 | 0.035897 | 0.040513 | 0.110398 | 0.101525 | 0.059829 | 0.735043 | -40.262985 | 83.713505 | 0.356354 |
| 10 | 11 | 0.500257 | 0.036662 | 1.024063 | 1.674354 | 0.061538 | 0.037796 | 0.100616 | 0.088766 | 0.102564 | 0.837607 | 2.406312 | 67.435371 | 0.358918 |
| 11 | 12 | 0.599897 | 0.034186 | 0.686228 | 1.510230 | 0.041237 | 0.035395 | 0.090753 | 0.079901 | 0.068376 | 0.905983 | -31.377214 | 51.023007 | 0.325655 |
| 12 | 13 | 0.700051 | 0.031902 | 0.256016 | 1.330794 | 0.015385 | 0.033091 | 0.079971 | 0.073204 | 0.025641 | 0.931624 | -74.398422 | 33.079369 | 0.246378 |
| 13 | 14 | 0.799692 | 0.029847 | 0.257335 | 1.197042 | 0.015464 | 0.030902 | 0.071933 | 0.067934 | 0.025641 | 0.957265 | -74.266455 | 19.704231 | 0.167647 |
| 14 | 15 | 0.899846 | 0.026364 | 0.085339 | 1.073308 | 0.005128 | 0.028236 | 0.064498 | 0.063515 | 0.008547 | 0.965812 | -91.466141 | 7.330816 | 0.070184 |
| 15 | 16 | 1.000000 | 0.002721 | 0.341354 | 1.000000 | 0.020513 | 0.021817 | 0.060092 | 0.059339 | 0.034188 | 1.000000 | -65.864563 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04853299693058677 RMSE: 0.220302058389355 LogLoss: 0.1880978797916073 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.5048270836396 Residual deviance: 2929.060184114909 AIC: 2959.060184114909 AUC: 0.7581723907564302 AUCPR: 0.2726502107034374 Gini: 0.5163447815128603 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.28898121191522086:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7034.0 | 284.0 | 0.0388 | (284.0/7318.0) |
| 1 | 1 | 277.0 | 191.0 | 0.5919 | (277.0/468.0) |
| 2 | Total | 7311.0 | 475.0 | 0.0721 | (561.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.288981 | 0.405090 | 134.0 |
| 1 | max f2 | 0.067266 | 0.438330 | 224.0 |
| 2 | max f0point5 | 0.326535 | 0.406184 | 109.0 |
| 3 | max accuracy | 0.431767 | 0.940406 | 38.0 |
| 4 | max precision | 0.632356 | 0.714286 | 6.0 |
| 5 | max recall | 0.019461 | 1.000000 | 386.0 |
| 6 | max specificity | 0.845317 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.288981 | 0.366755 | 134.0 |
| 8 | max min_per_class_accuracy | 0.040530 | 0.676141 | 293.0 |
| 9 | max mean_per_class_accuracy | 0.057291 | 0.712745 | 240.0 |
| 10 | max tns | 0.845317 | 7317.000000 | 0.0 |
| 11 | max fns | 0.845317 | 468.000000 | 0.0 |
| 12 | max fps | 0.002740 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019461 | 468.000000 | 386.0 |
| 14 | max tnr | 0.845317 | 0.999863 | 0.0 |
| 15 | max fnr | 0.845317 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002740 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019461 | 1.000000 | 386.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.412196 | 8.105084 | 8.105084 | 0.487179 | 0.497975 | 0.487179 | 0.497975 | 0.081197 | 0.081197 | 710.508437 | 710.508437 | 0.075731 |
| 1 | 2 | 0.020036 | 0.373868 | 6.612043 | 7.358563 | 0.397436 | 0.390472 | 0.442308 | 0.444224 | 0.066239 | 0.147436 | 561.204252 | 635.856345 | 0.135547 |
| 2 | 3 | 0.030054 | 0.353622 | 7.251918 | 7.323015 | 0.435897 | 0.363384 | 0.440171 | 0.417277 | 0.072650 | 0.220085 | 625.191760 | 632.301483 | 0.202184 |
| 3 | 4 | 0.040072 | 0.335418 | 6.185459 | 7.038626 | 0.371795 | 0.344769 | 0.423077 | 0.399150 | 0.061966 | 0.282051 | 518.545913 | 603.862590 | 0.257454 |
| 4 | 5 | 0.050090 | 0.317881 | 6.185459 | 6.867993 | 0.371795 | 0.326878 | 0.412821 | 0.384696 | 0.061966 | 0.344017 | 518.545913 | 586.799255 | 0.312724 |
| 5 | 6 | 0.100051 | 0.066008 | 2.993760 | 4.933363 | 0.179949 | 0.169556 | 0.296534 | 0.277264 | 0.149573 | 0.493590 | 199.376002 | 393.336296 | 0.418706 |
| 6 | 7 | 0.150013 | 0.051037 | 0.812592 | 3.560949 | 0.048843 | 0.056483 | 0.214041 | 0.203733 | 0.040598 | 0.534188 | -18.740799 | 256.094866 | 0.408744 |
| 7 | 8 | 0.200103 | 0.046403 | 0.767850 | 2.861778 | 0.046154 | 0.048524 | 0.172015 | 0.164881 | 0.038462 | 0.572650 | -23.214990 | 186.177765 | 0.396372 |
| 8 | 9 | 0.300026 | 0.041777 | 0.705672 | 2.143691 | 0.042416 | 0.043798 | 0.128853 | 0.124555 | 0.070513 | 0.643162 | -29.432799 | 114.369109 | 0.365081 |
| 9 | 10 | 0.400077 | 0.038778 | 0.726123 | 1.789185 | 0.043646 | 0.040294 | 0.107544 | 0.103483 | 0.072650 | 0.715812 | -27.387731 | 78.918522 | 0.335927 |
| 10 | 11 | 0.500000 | 0.036476 | 0.577368 | 1.547009 | 0.034704 | 0.037621 | 0.092987 | 0.090321 | 0.057692 | 0.773504 | -42.263200 | 54.700855 | 0.290995 |
| 11 | 12 | 0.600051 | 0.034331 | 0.790192 | 1.420819 | 0.047497 | 0.035385 | 0.085402 | 0.081161 | 0.079060 | 0.852564 | -20.980766 | 42.081852 | 0.268661 |
| 12 | 13 | 0.699974 | 0.032146 | 0.449064 | 1.282098 | 0.026992 | 0.033215 | 0.077064 | 0.074316 | 0.044872 | 0.897436 | -55.093600 | 28.209833 | 0.210090 |
| 13 | 14 | 0.800026 | 0.029837 | 0.405774 | 1.172505 | 0.024390 | 0.031014 | 0.070477 | 0.068901 | 0.040598 | 0.938034 | -59.422556 | 17.250509 | 0.146834 |
| 14 | 15 | 0.899949 | 0.026334 | 0.299376 | 1.075560 | 0.017995 | 0.028233 | 0.064650 | 0.064385 | 0.029915 | 0.967949 | -70.062400 | 7.555997 | 0.072349 |
| 15 | 16 | 1.000000 | 0.002172 | 0.320348 | 1.000000 | 0.019255 | 0.021982 | 0.060108 | 0.060143 | 0.032051 | 1.000000 | -67.965176 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.930639 | 0.021869548 | 0.9423077 | 0.9230769 | 0.97692305 | 0.90384614 | 0.9346154 | 0.90384614 | 0.9423077 | 0.93846154 | 0.9423077 | 0.93846154 | 0.9346154 | 0.91923076 | 0.9461538 | 0.9346154 | 0.9 | 0.95384616 | 0.9111969 | 0.93050194 | 0.9498069 | 0.9459459 | 0.9459459 | 0.95752895 | 0.9266409 | 0.8880309 | 0.95752895 | 0.93436295 | 0.9111969 | 0.88030887 | 0.9111969 | 0.93436295 |
| 1 | auc | 0.76002204 | 0.060716234 | 0.7518367 | 0.81395835 | 0.8196 | 0.73185486 | 0.7804 | 0.7714839 | 0.76462585 | 0.65456986 | 0.7284836 | 0.7049731 | 0.83972126 | 0.7740586 | 0.66334474 | 0.8304303 | 0.739375 | 0.85980284 | 0.78475034 | 0.80921054 | 0.76762277 | 0.7761641 | 0.7978142 | 0.91096437 | 0.7194561 | 0.7451754 | 0.7177419 | 0.718107 | 0.7010209 | 0.7484568 | 0.7408848 | 0.6347737 |
| 2 | err | 0.06936096 | 0.021869548 | 0.057692308 | 0.07692308 | 0.023076924 | 0.09615385 | 0.06538462 | 0.09615385 | 0.057692308 | 0.06153846 | 0.057692308 | 0.06153846 | 0.06538462 | 0.08076923 | 0.053846154 | 0.06538462 | 0.1 | 0.046153847 | 0.08880309 | 0.06949807 | 0.05019305 | 0.054054055 | 0.054054055 | 0.042471044 | 0.07335907 | 0.11196911 | 0.042471044 | 0.06563707 | 0.08880309 | 0.11969112 | 0.08880309 | 0.06563707 |
| 3 | err_count | 18.0 | 5.6690326 | 15.0 | 20.0 | 6.0 | 25.0 | 17.0 | 25.0 | 15.0 | 16.0 | 15.0 | 16.0 | 17.0 | 21.0 | 14.0 | 17.0 | 26.0 | 12.0 | 23.0 | 18.0 | 13.0 | 14.0 | 14.0 | 11.0 | 19.0 | 29.0 | 11.0 | 17.0 | 23.0 | 31.0 | 23.0 | 17.0 |
| 4 | f0point5 | 0.45134598 | 0.12414881 | 0.5063291 | 0.5 | 0.7352941 | 0.20833333 | 0.3488372 | 0.3539823 | 0.49295774 | 0.36764705 | 0.5 | 0.33333334 | 0.44444445 | 0.47058824 | 0.36585367 | 0.48913044 | 0.37037036 | 0.42553192 | 0.28846154 | 0.49295774 | 0.61728394 | 0.6081081 | 0.52238804 | 0.7425743 | 0.5208333 | 0.34351146 | 0.49019608 | 0.48913044 | 0.41322315 | 0.24193548 | 0.3809524 | 0.47619048 |
| 5 | f1 | 0.45171508 | 0.10435273 | 0.516129 | 0.5 | 0.625 | 0.24242425 | 0.41379312 | 0.3902439 | 0.4827586 | 0.3846154 | 0.44444445 | 0.33333334 | 0.4848485 | 0.43243244 | 0.3 | 0.51428574 | 0.3809524 | 0.4 | 0.34285715 | 0.4375 | 0.6060606 | 0.5625 | 0.5 | 0.73170733 | 0.51282054 | 0.38297874 | 0.47619048 | 0.51428574 | 0.4651163 | 0.27906978 | 0.41025642 | 0.4848485 |
| 6 | f2 | 0.45964393 | 0.09599599 | 0.5263158 | 0.5 | 0.54347825 | 0.28985506 | 0.5084746 | 0.4347826 | 0.47297296 | 0.4032258 | 0.4 | 0.33333334 | 0.53333336 | 0.4 | 0.2542373 | 0.5421687 | 0.39215687 | 0.3773585 | 0.4225352 | 0.39325842 | 0.5952381 | 0.5232558 | 0.47945204 | 0.72115386 | 0.5050505 | 0.43269232 | 0.46296296 | 0.5421687 | 0.5319149 | 0.32967034 | 0.44444445 | 0.49382716 |
| 7 | lift_top_group | 8.100411 | 5.807636 | 11.555555 | 13.0 | 26.0 | 0.0 | 8.666667 | 0.0 | 5.7777777 | 0.0 | 10.833333 | 0.0 | 6.1904764 | 8.253968 | 6.6666665 | 5.4166665 | 8.666667 | 15.757576 | 14.388889 | 4.5438595 | 10.156863 | 4.796296 | 5.7555556 | 12.333333 | 12.95 | 9.087719 | 0.0 | 5.3958335 | 10.156863 | 5.3958335 | 5.0784316 | 16.1875 |
| 8 | logloss | 0.18674502 | 0.029822515 | 0.17069869 | 0.21911739 | 0.12035521 | 0.17849283 | 0.13065125 | 0.20998824 | 0.19075987 | 0.16730343 | 0.19455883 | 0.1733792 | 0.16657902 | 0.23909563 | 0.18415388 | 0.17670736 | 0.24333078 | 0.14939362 | 0.15717643 | 0.22009419 | 0.1792355 | 0.19008382 | 0.18095805 | 0.17145722 | 0.21136793 | 0.2311544 | 0.14888732 | 0.18417783 | 0.19513975 | 0.21703881 | 0.20863463 | 0.19237944 |
| 9 | max_per_class_error | 0.5299877 | 0.10026924 | 0.46666667 | 0.5 | 0.5 | 0.6666667 | 0.4 | 0.5294118 | 0.53333336 | 0.5833333 | 0.625 | 0.6666667 | 0.42857143 | 0.61904764 | 0.7692308 | 0.4375 | 0.6 | 0.6363636 | 0.5 | 0.6315789 | 0.4117647 | 0.5 | 0.53333336 | 0.2857143 | 0.5 | 0.5263158 | 0.54545456 | 0.4375 | 0.4117647 | 0.625 | 0.5294118 | 0.5 |
| 10 | mcc | 0.42116952 | 0.109933004 | 0.48577398 | 0.45833334 | 0.63527125 | 0.20389245 | 0.4049163 | 0.34565836 | 0.452549 | 0.3535887 | 0.42323852 | 0.30107528 | 0.456794 | 0.39398798 | 0.2889281 | 0.48154575 | 0.32713228 | 0.37829927 | 0.31861913 | 0.41008145 | 0.5795864 | 0.5389676 | 0.47293204 | 0.7089101 | 0.47336474 | 0.33125597 | 0.4546702 | 0.48140234 | 0.43026647 | 0.22735466 | 0.36662328 | 0.45006707 |
| 11 | mean_per_class_accuracy | 0.71489567 | 0.050923437 | 0.75034016 | 0.7291667 | 0.748 | 0.63239247 | 0.774 | 0.7023723 | 0.7190476 | 0.69018817 | 0.6772541 | 0.6505376 | 0.76335657 | 0.6737398 | 0.60728747 | 0.7607582 | 0.67083335 | 0.671778 | 0.715587 | 0.67171055 | 0.78172094 | 0.7396265 | 0.7210382 | 0.8466387 | 0.73117155 | 0.6972588 | 0.71719205 | 0.7606739 | 0.7610598 | 0.64429015 | 0.7063685 | 0.7314815 |
| 12 | mean_per_class_error | 0.2851043 | 0.050923437 | 0.24965987 | 0.27083334 | 0.252 | 0.36760753 | 0.226 | 0.2976277 | 0.2809524 | 0.30981183 | 0.3227459 | 0.34946236 | 0.23664343 | 0.3262602 | 0.39271256 | 0.23924181 | 0.32916668 | 0.32822198 | 0.28441295 | 0.32828948 | 0.21827905 | 0.26037344 | 0.27896175 | 0.15336135 | 0.26882845 | 0.30274123 | 0.28280792 | 0.23932613 | 0.23894021 | 0.35570988 | 0.2936315 | 0.2685185 |
| 13 | mse | 0.04797019 | 0.009078655 | 0.04358966 | 0.058318257 | 0.027621632 | 0.043287102 | 0.030649375 | 0.05566111 | 0.049142234 | 0.040691342 | 0.04926661 | 0.04260004 | 0.04279749 | 0.064395726 | 0.045083065 | 0.04762869 | 0.06452526 | 0.037828527 | 0.038054656 | 0.0591703 | 0.046160378 | 0.049872898 | 0.046676427 | 0.047450494 | 0.05372177 | 0.061398324 | 0.03645284 | 0.046958476 | 0.050906476 | 0.056149296 | 0.05480715 | 0.048240118 |
| 14 | null_deviance | 118.01683 | 18.278048 | 114.7255 | 142.31174 | 87.37807 | 98.28862 | 87.37807 | 125.73113 | 114.7255 | 98.28862 | 120.223526 | 98.28862 | 109.23703 | 147.85797 | 103.75808 | 120.223526 | 142.31174 | 92.82863 | 98.16257 | 136.65318 | 125.607956 | 131.12575 | 114.60118 | 147.73709 | 142.19029 | 136.65318 | 92.701996 | 120.09978 | 125.607956 | 120.09978 | 125.607956 | 120.09978 |
| 15 | pr_auc | 0.30866387 | 0.13544694 | 0.37403858 | 0.43979844 | 0.55182123 | 0.106295615 | 0.288301 | 0.19137917 | 0.27292496 | 0.14933935 | 0.31265703 | 0.14846396 | 0.32575917 | 0.34667712 | 0.13388416 | 0.28803632 | 0.2771598 | 0.2592442 | 0.31386915 | 0.28422615 | 0.4168521 | 0.41833597 | 0.25909868 | 0.75087446 | 0.48591277 | 0.2805382 | 0.17770143 | 0.29784143 | 0.33956137 | 0.16282444 | 0.22048073 | 0.3860197 |
| 16 | precision | 0.45527977 | 0.14317499 | 0.5 | 0.5 | 0.8333333 | 0.1904762 | 0.31578946 | 0.33333334 | 0.5 | 0.35714287 | 0.54545456 | 0.33333334 | 0.42105263 | 0.5 | 0.42857143 | 0.47368422 | 0.36363637 | 0.44444445 | 0.26086956 | 0.53846157 | 0.625 | 0.64285713 | 0.53846157 | 0.75 | 0.5263158 | 0.32142857 | 0.5 | 0.47368422 | 0.3846154 | 0.22222222 | 0.36363637 | 0.47058824 |
| 17 | r2 | 0.14327149 | 0.07704196 | 0.19818752 | 0.17868455 | 0.25311106 | 0.016731145 | 0.1712409 | 0.089157395 | 0.09605036 | 0.075693935 | 0.14692035 | 0.032337848 | 0.1599563 | 0.13266571 | 0.05088283 | 0.1752819 | 0.09126922 | 0.0663715 | 0.13875018 | 0.12956081 | 0.24733 | 0.22878657 | 0.1445078 | 0.36313993 | 0.24608578 | 0.09678485 | 0.10363162 | 0.18980928 | 0.16994233 | 0.031236872 | 0.10633973 | 0.16769667 |
| 18 | recall | 0.47001234 | 0.10026924 | 0.53333336 | 0.5 | 0.5 | 0.33333334 | 0.6 | 0.47058824 | 0.46666667 | 0.41666666 | 0.375 | 0.33333334 | 0.5714286 | 0.3809524 | 0.23076923 | 0.5625 | 0.4 | 0.36363637 | 0.5 | 0.36842105 | 0.5882353 | 0.5 | 0.46666667 | 0.71428573 | 0.5 | 0.47368422 | 0.45454547 | 0.5625 | 0.5882353 | 0.375 | 0.47058824 | 0.5 |
| 19 | residual_deviance | 96.92822 | 15.459083 | 88.76331 | 113.94104 | 62.58471 | 92.81627 | 67.93865 | 109.193886 | 99.19513 | 86.99778 | 101.170586 | 90.15719 | 86.62109 | 124.32973 | 95.76002 | 91.887825 | 126.532005 | 77.684685 | 81.4174 | 114.00879 | 92.843994 | 98.46342 | 93.73627 | 88.814835 | 109.488594 | 119.737976 | 77.123634 | 95.40411 | 101.08239 | 112.4261 | 108.07274 | 99.65256 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:13:29 | 0.000 sec | 2 | .85E1 | 15.0 | 0.452095 | 0.452053 | 0.452447 | 0.012813 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:13:29 | 0.003 sec | 4 | .53E1 | 15.0 | 0.450645 | 0.450628 | 0.451062 | 0.012758 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:13:29 | 0.005 sec | 6 | .33E1 | 15.0 | 0.448368 | 0.448389 | 0.448885 | 0.012673 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:13:29 | 0.012 sec | 8 | .2E1 | 15.0 | 0.444829 | 0.444906 | 0.445497 | 0.012543 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:13:29 | 0.015 sec | 10 | .13E1 | 15.0 | 0.439503 | 0.439655 | 0.440386 | 0.012351 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:13:29 | 0.017 sec | 12 | .78E0 | 15.0 | 0.431837 | 0.432078 | 0.432999 | 0.012085 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:13:29 | 0.020 sec | 14 | .49E0 | 15.0 | 0.421645 | 0.421966 | 0.423113 | 0.011755 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:13:29 | 0.023 sec | 16 | .3E0 | 15.0 | 0.409751 | 0.410091 | 0.411463 | 0.011409 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:13:29 | 0.025 sec | 18 | .19E0 | 15.0 | 0.398122 | 0.398352 | 0.399951 | 0.011129 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:13:29 | 0.028 sec | 20 | .12E0 | 15.0 | 0.388728 | 0.388691 | 0.390596 | 0.010967 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:13:29 | 0.031 sec | 22 | .72E-1 | 15.0 | 0.382229 | 0.381784 | 0.384164 | 0.010909 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:13:29 | 0.033 sec | 24 | .45E-1 | 15.0 | 0.378167 | 0.377215 | 0.38024 | 0.01091 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:13:29 | 0.036 sec | 26 | .28E-1 | 15.0 | 0.375751 | 0.374251 | 0.378026 | 0.010934 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:13:29 | 0.039 sec | 28 | .17E-1 | 15.0 | 0.374324 | 0.372296 | 0.376831 | 0.01096 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:13:29 | 0.332 sec | 29 | None | NaN | 29.0 | 0.219559 | 0.186739 | 0.146714 | 0.769637 | 0.287591 | 8.531668 | 0.071153 | 0.219236 | 0.185499 | 0.149019 | 0.785458 | 0.287834 | 8.320513 | 0.071392 | ||||||
| 15 | 2021-07-15 20:13:29 | 0.041 sec | 30 | .11E-1 | 15.0 | 0.373478 | 0.370997 | 0.376214 | 0.01098 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:13:29 | 0.044 sec | 32 | .67E-2 | 15.0 | 0.372977 | 0.370141 | 0.376375 | 0.010984 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:13:29 | 0.046 sec | 34 | .42E-2 | 15.0 | 0.372687 | 0.369595 | 0.377789 | 0.011389 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:13:29 | 0.048 sec | 35 | .26E-2 | 15.0 | 0.372526 | 0.369264 | 0.380003 | 0.011253 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.541528 | 1.000000 | 0.275240 |
| 1 | Minimum_Transaction_Amount | 0.196883 | 0.363570 | 0.100069 |
| 2 | Average_Transaction_Frequency | 0.196178 | 0.362268 | 0.099711 |
| 3 | Channel_ID | 0.190374 | 0.351550 | 0.096761 |
| 4 | Merchant_ID | 0.170791 | 0.315387 | 0.086807 |
| 5 | Card_Type.1 | 0.149322 | 0.275741 | 0.075895 |
| 6 | Card_Type.0 | 0.147937 | 0.273185 | 0.075191 |
| 7 | Transaction_Amount | 0.111745 | 0.206351 | 0.056796 |
| 8 | Transaction_Date | 0.075505 | 0.139430 | 0.038377 |
| 9 | Average_Transaction_Amount | 0.056479 | 0.104296 | 0.028706 |
| 10 | Maximum_Transaction_Amount | 0.050551 | 0.093348 | 0.025693 |
| 11 | Month | 0.045590 | 0.084187 | 0.023172 |
| 12 | Day | 0.025205 | 0.046545 | 0.012811 |
| 13 | City_ID | 0.009387 | 0.017334 | 0.004771 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201331 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01028 ) | nlambda = 30, lambda.max = 8.0972, lambda.min = 0.01028, lambda.1s... | 14 | 14 | 30 | automl_training_py_406_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04879459012847571 RMSE: 0.2208949753355103 LogLoss: 0.18932300958708873 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793851 Residual deviance: 2948.1379052901466 AIC: 2978.1379052901466 AUC: 0.762770729240393 AUCPR: 0.28025048903302185 Gini: 0.525541458480786 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.25344026246357176:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7019.0 | 299.0 | 0.0409 | (299.0/7318.0) |
| 1 | 1 | 280.0 | 188.0 | 0.5983 | (280.0/468.0) |
| 2 | Total | 7299.0 | 487.0 | 0.0744 | (579.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.253440 | 0.393717 | 143.0 |
| 1 | max f2 | 0.064820 | 0.424041 | 228.0 |
| 2 | max f0point5 | 0.326081 | 0.408105 | 101.0 |
| 3 | max accuracy | 0.430633 | 0.940663 | 37.0 |
| 4 | max precision | 0.858774 | 1.000000 | 0.0 |
| 5 | max recall | 0.019830 | 1.000000 | 380.0 |
| 6 | max specificity | 0.858774 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.289728 | 0.354672 | 125.0 |
| 8 | max min_per_class_accuracy | 0.043250 | 0.683761 | 285.0 |
| 9 | max mean_per_class_accuracy | 0.059614 | 0.704533 | 237.0 |
| 10 | max tns | 0.858774 | 7318.000000 | 0.0 |
| 11 | max fns | 0.858774 | 467.000000 | 0.0 |
| 12 | max fps | 0.001533 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019830 | 468.000000 | 380.0 |
| 14 | max tnr | 0.858774 | 1.000000 | 0.0 |
| 15 | max fnr | 0.858774 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001533 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019830 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.407966 | 8.531668 | 8.531668 | 0.512821 | 0.483869 | 0.512821 | 0.483869 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.373111 | 7.891793 | 8.211730 | 0.474359 | 0.389034 | 0.493590 | 0.436451 | 0.079060 | 0.164530 | 689.179268 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.349422 | 7.251918 | 7.891793 | 0.435897 | 0.360278 | 0.474359 | 0.411060 | 0.072650 | 0.237179 | 625.191760 | 689.179268 | 0.220372 |
| 3 | 4 | 0.040072 | 0.328127 | 5.972167 | 7.411886 | 0.358974 | 0.338275 | 0.445513 | 0.392864 | 0.059829 | 0.297009 | 497.216743 | 641.188637 | 0.273368 |
| 4 | 5 | 0.050090 | 0.304528 | 5.332292 | 6.995968 | 0.320513 | 0.316923 | 0.420513 | 0.377676 | 0.053419 | 0.350427 | 433.229235 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.068606 | 2.437776 | 4.719797 | 0.146530 | 0.153244 | 0.283697 | 0.265604 | 0.121795 | 0.472222 | 143.777602 | 371.979746 | 0.395972 |
| 6 | 7 | 0.150013 | 0.054340 | 1.026432 | 3.489730 | 0.061697 | 0.059773 | 0.209760 | 0.197052 | 0.051282 | 0.523504 | 2.643201 | 248.972969 | 0.397377 |
| 7 | 8 | 0.200103 | 0.049488 | 1.066458 | 2.883134 | 0.064103 | 0.051655 | 0.173299 | 0.160657 | 0.053419 | 0.576923 | 6.645847 | 188.313420 | 0.400919 |
| 8 | 9 | 0.300026 | 0.044401 | 0.876744 | 2.214910 | 0.052699 | 0.046609 | 0.133134 | 0.122673 | 0.087607 | 0.664530 | -12.325599 | 121.491007 | 0.387815 |
| 9 | 10 | 0.400077 | 0.040799 | 0.576627 | 1.805208 | 0.034660 | 0.042544 | 0.108507 | 0.102634 | 0.057692 | 0.722222 | -42.337316 | 80.520778 | 0.342747 |
| 10 | 11 | 0.500000 | 0.038047 | 0.641520 | 1.572650 | 0.038560 | 0.039378 | 0.094529 | 0.089993 | 0.064103 | 0.786325 | -35.847999 | 57.264957 | 0.304636 |
| 11 | 12 | 0.600051 | 0.035469 | 0.619340 | 1.413697 | 0.037227 | 0.036694 | 0.084974 | 0.081106 | 0.061966 | 0.848291 | -38.066006 | 41.369662 | 0.264115 |
| 12 | 13 | 0.699974 | 0.032907 | 0.513216 | 1.285151 | 0.030848 | 0.034181 | 0.077248 | 0.074407 | 0.051282 | 0.899573 | -48.678400 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.029907 | 0.448488 | 1.180518 | 0.026958 | 0.031494 | 0.070958 | 0.069041 | 0.044872 | 0.944444 | -55.151246 | 18.051765 | 0.153655 |
| 14 | 15 | 0.899949 | 0.025845 | 0.320760 | 1.085057 | 0.019280 | 0.028030 | 0.065220 | 0.064487 | 0.032051 | 0.976496 | -67.924000 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.001187 | 0.234922 | 1.000000 | 0.014121 | 0.020718 | 0.060108 | 0.060108 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.045781070849059934 RMSE: 0.21396511596299975 LogLoss: 0.17497793967337177 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311477 Residual deviance: 681.3640970881094 AIC: 711.3640970881094 AUC: 0.81067675493905 AUCPR: 0.3299005769579236 Gini: 0.6213535098781 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.18329276503633807:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1738.0 | 92.0 | 0.0503 | (92.0/1830.0) |
| 1 | 1 | 52.0 | 65.0 | 0.4444 | (52.0/117.0) |
| 2 | Total | 1790.0 | 157.0 | 0.074 | (144.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.183293 | 0.474453 | 126.0 |
| 1 | max f2 | 0.106203 | 0.524257 | 139.0 |
| 2 | max f0point5 | 0.311894 | 0.445633 | 88.0 |
| 3 | max accuracy | 0.569185 | 0.941962 | 3.0 |
| 4 | max precision | 0.791081 | 1.000000 | 0.0 |
| 5 | max recall | 0.021196 | 1.000000 | 372.0 |
| 6 | max specificity | 0.791081 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.183293 | 0.441038 | 126.0 |
| 8 | max min_per_class_accuracy | 0.045578 | 0.728415 | 235.0 |
| 9 | max mean_per_class_accuracy | 0.065243 | 0.764621 | 167.0 |
| 10 | max tns | 0.791081 | 1830.000000 | 0.0 |
| 11 | max fns | 0.791081 | 116.000000 | 0.0 |
| 12 | max fps | 0.001490 | 1830.000000 | 399.0 |
| 13 | max tps | 0.021196 | 117.000000 | 372.0 |
| 14 | max tnr | 0.791081 | 1.000000 | 0.0 |
| 15 | max fnr | 0.791081 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001490 | 1.000000 | 399.0 |
| 17 | max tpr | 0.021196 | 1.000000 | 372.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.44 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.417907 | 8.320513 | 8.320513 | 0.500000 | 0.514067 | 0.500000 | 0.514067 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.376596 | 4.379217 | 6.400394 | 0.263158 | 0.395982 | 0.384615 | 0.456539 | 0.042735 | 0.128205 | 337.921727 | 540.039448 | 0.115090 |
| 2 | 3 | 0.030303 | 0.348128 | 7.488462 | 6.769231 | 0.450000 | 0.362587 | 0.406780 | 0.424691 | 0.076923 | 0.205128 | 648.846154 | 576.923077 | 0.186003 |
| 3 | 4 | 0.040062 | 0.335751 | 10.510121 | 7.680473 | 0.631579 | 0.341273 | 0.461538 | 0.404371 | 0.102564 | 0.307692 | 951.012146 | 668.047337 | 0.284741 |
| 4 | 5 | 0.050334 | 0.324048 | 6.656410 | 7.471481 | 0.400000 | 0.328876 | 0.448980 | 0.388964 | 0.068376 | 0.376068 | 565.641026 | 647.148090 | 0.346560 |
| 5 | 6 | 0.100154 | 0.073589 | 4.117367 | 5.803024 | 0.247423 | 0.213678 | 0.348718 | 0.301770 | 0.205128 | 0.581197 | 311.736717 | 480.302433 | 0.511798 |
| 6 | 7 | 0.149974 | 0.056214 | 0.686228 | 4.103267 | 0.041237 | 0.062239 | 0.246575 | 0.222200 | 0.034188 | 0.615385 | -31.377214 | 310.326660 | 0.495166 |
| 7 | 8 | 0.200308 | 0.050330 | 0.679226 | 3.242867 | 0.040816 | 0.052856 | 0.194872 | 0.179647 | 0.034188 | 0.649573 | -32.077446 | 224.286654 | 0.477988 |
| 8 | 9 | 0.299949 | 0.045512 | 0.857785 | 2.450562 | 0.051546 | 0.047712 | 0.147260 | 0.135819 | 0.085470 | 0.735043 | -14.221517 | 145.056200 | 0.462912 |
| 9 | 10 | 0.400103 | 0.041787 | 0.512032 | 1.965307 | 0.030769 | 0.043528 | 0.118100 | 0.112717 | 0.051282 | 0.786325 | -48.796844 | 96.530726 | 0.410915 |
| 10 | 11 | 0.500257 | 0.038918 | 0.341354 | 1.640183 | 0.020513 | 0.040348 | 0.098563 | 0.098228 | 0.034188 | 0.820513 | -65.864563 | 64.018323 | 0.340731 |
| 11 | 12 | 0.599897 | 0.036014 | 0.772006 | 1.495983 | 0.046392 | 0.037429 | 0.089897 | 0.088130 | 0.076923 | 0.897436 | -22.799366 | 49.598261 | 0.316562 |
| 12 | 13 | 0.700051 | 0.033294 | 0.426693 | 1.343003 | 0.025641 | 0.034737 | 0.080704 | 0.080491 | 0.042735 | 0.940171 | -57.330703 | 34.300280 | 0.255471 |
| 13 | 14 | 0.799692 | 0.030047 | 0.257335 | 1.207730 | 0.015464 | 0.031722 | 0.072575 | 0.074415 | 0.025641 | 0.965812 | -74.266455 | 20.773018 | 0.176741 |
| 14 | 15 | 0.899846 | 0.025840 | 0.170677 | 1.092305 | 0.010256 | 0.028178 | 0.065639 | 0.069268 | 0.017094 | 0.982906 | -82.932281 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.001431 | 0.170677 | 1.000000 | 0.010256 | 0.020784 | 0.060092 | 0.064412 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04898284062638916 RMSE: 0.22132067374375392 LogLoss: 0.19019902855039397 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.402415686062 Residual deviance: 2961.779272586735 AIC: 2991.779272586735 AUC: 0.757309718689194 AUCPR: 0.2699001838841575 Gini: 0.5146194373783879 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2596779605407001:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7034.0 | 284.0 | 0.0388 | (284.0/7318.0) |
| 1 | 1 | 284.0 | 184.0 | 0.6068 | (284.0/468.0) |
| 2 | Total | 7318.0 | 468.0 | 0.073 | (568.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.259678 | 0.393162 | 143.0 |
| 1 | max f2 | 0.064135 | 0.416821 | 230.0 |
| 2 | max f0point5 | 0.317021 | 0.403139 | 107.0 |
| 3 | max accuracy | 0.422557 | 0.940534 | 37.0 |
| 4 | max precision | 0.542777 | 0.615385 | 11.0 |
| 5 | max recall | 0.019620 | 1.000000 | 382.0 |
| 6 | max specificity | 0.863122 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.259678 | 0.354354 | 143.0 |
| 8 | max min_per_class_accuracy | 0.043966 | 0.679487 | 284.0 |
| 9 | max mean_per_class_accuracy | 0.058781 | 0.702092 | 240.0 |
| 10 | max tns | 0.863122 | 7317.000000 | 0.0 |
| 11 | max fns | 0.863122 | 468.000000 | 0.0 |
| 12 | max fps | 0.001495 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019620 | 468.000000 | 382.0 |
| 14 | max tnr | 0.863122 | 0.999863 | 0.0 |
| 15 | max fnr | 0.863122 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001495 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019620 | 1.000000 | 382.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.04 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.406428 | 8.744959 | 8.744959 | 0.525641 | 0.483191 | 0.525641 | 0.483191 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.371459 | 6.612043 | 7.678501 | 0.397436 | 0.387515 | 0.461538 | 0.435353 | 0.066239 | 0.153846 | 561.204252 | 667.850099 | 0.142368 |
| 2 | 3 | 0.030054 | 0.346946 | 7.038626 | 7.465209 | 0.423077 | 0.358315 | 0.448718 | 0.409673 | 0.070513 | 0.224359 | 603.862590 | 646.520929 | 0.206731 |
| 3 | 4 | 0.040072 | 0.325645 | 6.398751 | 7.198595 | 0.384615 | 0.336197 | 0.432692 | 0.391304 | 0.064103 | 0.288462 | 539.875082 | 619.859467 | 0.264275 |
| 4 | 5 | 0.050090 | 0.301008 | 5.545584 | 6.867993 | 0.333333 | 0.313755 | 0.412821 | 0.375795 | 0.055556 | 0.344017 | 454.558405 | 586.799255 | 0.312724 |
| 5 | 6 | 0.100051 | 0.066771 | 2.437776 | 4.655728 | 0.146530 | 0.145555 | 0.279846 | 0.260823 | 0.121795 | 0.465812 | 143.777602 | 365.572781 | 0.389152 |
| 6 | 7 | 0.150527 | 0.060050 | 1.227648 | 3.506210 | 0.073791 | 0.061061 | 0.210751 | 0.193838 | 0.061966 | 0.527778 | 22.764838 | 250.620971 | 0.401377 |
| 7 | 8 | 0.200103 | 0.051932 | 0.603406 | 2.787030 | 0.036269 | 0.055278 | 0.167522 | 0.159509 | 0.029915 | 0.557692 | -39.659448 | 178.702972 | 0.380458 |
| 8 | 9 | 0.300026 | 0.045457 | 0.962280 | 2.179301 | 0.057841 | 0.048192 | 0.130993 | 0.122435 | 0.096154 | 0.653846 | -3.771999 | 117.930058 | 0.376448 |
| 9 | 10 | 0.400077 | 0.041469 | 0.619340 | 1.789185 | 0.037227 | 0.043453 | 0.107544 | 0.102683 | 0.061966 | 0.715812 | -38.066006 | 78.918522 | 0.335927 |
| 10 | 11 | 0.500000 | 0.038552 | 0.641520 | 1.559829 | 0.038560 | 0.039908 | 0.093758 | 0.090138 | 0.064103 | 0.779915 | -35.847999 | 55.982906 | 0.297816 |
| 11 | 12 | 0.600051 | 0.035841 | 0.640696 | 1.406575 | 0.038511 | 0.037218 | 0.084546 | 0.081314 | 0.064103 | 0.844017 | -35.930351 | 40.657472 | 0.259568 |
| 12 | 13 | 0.699974 | 0.033237 | 0.555984 | 1.285151 | 0.033419 | 0.034527 | 0.077248 | 0.074635 | 0.055556 | 0.899573 | -44.401600 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.030173 | 0.427131 | 1.177847 | 0.025674 | 0.031818 | 0.070798 | 0.069280 | 0.042735 | 0.942308 | -57.286901 | 17.784680 | 0.151381 |
| 14 | 15 | 0.899949 | 0.026012 | 0.299376 | 1.080309 | 0.017995 | 0.028322 | 0.064935 | 0.064733 | 0.029915 | 0.972222 | -70.062400 | 8.030858 | 0.076896 |
| 15 | 16 | 1.000000 | 0.001074 | 0.277635 | 1.000000 | 0.016688 | 0.020956 | 0.060108 | 0.060353 | 0.027778 | 1.000000 | -72.236486 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93257153 | 0.02331159 | 0.9346154 | 0.96153843 | 0.9576923 | 0.9461538 | 0.9230769 | 0.9346154 | 0.8730769 | 0.95 | 0.90384614 | 0.9423077 | 0.93846154 | 0.9153846 | 0.9269231 | 0.9269231 | 0.91923076 | 0.9653846 | 0.9498069 | 0.8687259 | 0.90733594 | 0.9498069 | 0.9266409 | 0.95366794 | 0.94208497 | 0.93436295 | 0.93050194 | 0.9150579 | 0.969112 | 0.93822396 | 0.93822396 | 0.93436295 |
| 1 | auc | 0.7620399 | 0.07268435 | 0.8141696 | 0.83623695 | 0.8118966 | 0.7395833 | 0.747483 | 0.7540547 | 0.77512175 | 0.78192204 | 0.6812452 | 0.7154471 | 0.7537415 | 0.72996515 | 0.7735417 | 0.8298755 | 0.8077306 | 0.8919876 | 0.81988746 | 0.7388577 | 0.4979424 | 0.6433873 | 0.7216821 | 0.80874634 | 0.7282799 | 0.7269076 | 0.78950435 | 0.75438595 | 0.7710843 | 0.81374353 | 0.74185705 | 0.86092895 |
| 2 | err | 0.06742847 | 0.02331159 | 0.06538462 | 0.03846154 | 0.042307694 | 0.053846154 | 0.07692308 | 0.06538462 | 0.12692308 | 0.05 | 0.09615385 | 0.057692308 | 0.06153846 | 0.08461539 | 0.073076926 | 0.073076926 | 0.08076923 | 0.034615386 | 0.05019305 | 0.13127413 | 0.09266409 | 0.05019305 | 0.07335907 | 0.046332046 | 0.057915058 | 0.06563707 | 0.06949807 | 0.08494209 | 0.03088803 | 0.06177606 | 0.06177606 | 0.06563707 |
| 3 | err_count | 17.5 | 6.0500784 | 17.0 | 10.0 | 11.0 | 14.0 | 20.0 | 17.0 | 33.0 | 13.0 | 25.0 | 15.0 | 16.0 | 22.0 | 19.0 | 19.0 | 21.0 | 9.0 | 13.0 | 34.0 | 24.0 | 13.0 | 19.0 | 12.0 | 15.0 | 17.0 | 18.0 | 22.0 | 8.0 | 16.0 | 16.0 | 17.0 |
| 4 | f0point5 | 0.45333487 | 0.121485546 | 0.44444445 | 0.6481481 | 0.5660377 | 0.6730769 | 0.3846154 | 0.49382716 | 0.19607843 | 0.375 | 0.40816328 | 0.43103448 | 0.39215687 | 0.34313726 | 0.5 | 0.52845526 | 0.4950495 | 0.5263158 | 0.5072464 | 0.40123457 | 0.15625 | 0.375 | 0.41095892 | 0.5714286 | 0.43103448 | 0.3488372 | 0.32258064 | 0.42105263 | 0.6 | 0.67164177 | 0.4918033 | 0.4854369 |
| 5 | f1 | 0.4395232 | 0.106996834 | 0.4848485 | 0.5833333 | 0.5217391 | 0.5 | 0.4117647 | 0.4848485 | 0.26666668 | 0.31578946 | 0.3902439 | 0.4 | 0.33333334 | 0.3888889 | 0.45714286 | 0.5777778 | 0.4878049 | 0.5714286 | 0.5185185 | 0.43333334 | 0.14285715 | 0.31578946 | 0.38709676 | 0.5714286 | 0.4 | 0.41379312 | 0.30769232 | 0.42105263 | 0.6 | 0.5294118 | 0.42857143 | 0.5405405 |
| 6 | f2 | 0.43898514 | 0.11757075 | 0.53333336 | 0.530303 | 0.48387095 | 0.39772728 | 0.443038 | 0.47619048 | 0.41666666 | 0.27272728 | 0.37383178 | 0.37313432 | 0.28985506 | 0.44871795 | 0.42105263 | 0.6372549 | 0.48076922 | 0.625 | 0.530303 | 0.4710145 | 0.13157895 | 0.27272728 | 0.36585367 | 0.5714286 | 0.37313432 | 0.5084746 | 0.29411766 | 0.42105263 | 0.6 | 0.4368932 | 0.37974682 | 0.6097561 |
| 7 | lift_top_group | 8.055964 | 4.8389373 | 6.1904764 | 12.380953 | 13.333333 | 8.666667 | 0.0 | 5.098039 | 0.0 | 7.2222223 | 7.878788 | 12.380953 | 11.555555 | 12.380953 | 13.0 | 0.0 | 8.253968 | 9.62963 | 6.6410255 | 6.6410255 | 0.0 | 7.1944447 | 10.156863 | 18.5 | 6.1666665 | 17.266666 | 0.0 | 9.087719 | 8.633333 | 7.5072465 | 10.156863 | 5.7555556 |
| 8 | logloss | 0.18887521 | 0.04136169 | 0.16410452 | 0.14933002 | 0.16463204 | 0.23194468 | 0.18658024 | 0.19865295 | 0.15206435 | 0.16566308 | 0.2614211 | 0.17814285 | 0.20364782 | 0.18140955 | 0.22771919 | 0.19187115 | 0.22636528 | 0.106269345 | 0.15185198 | 0.29309857 | 0.23121713 | 0.17500417 | 0.20871337 | 0.14632522 | 0.1842715 | 0.13767691 | 0.18749861 | 0.22620243 | 0.13217394 | 0.23906957 | 0.2071656 | 0.15616916 |
| 9 | max_per_class_error | 0.5511981 | 0.14306186 | 0.42857143 | 0.5 | 0.53846157 | 0.65 | 0.53333336 | 0.5294118 | 0.33333334 | 0.75 | 0.6363636 | 0.64285713 | 0.73333335 | 0.5 | 0.6 | 0.31578946 | 0.52380955 | 0.33333334 | 0.46153846 | 0.5 | 0.875 | 0.75 | 0.64705884 | 0.42857143 | 0.64285713 | 0.4 | 0.71428573 | 0.57894737 | 0.4 | 0.6086956 | 0.64705884 | 0.33333334 |
| 10 | mcc | 0.41568193 | 0.11190577 | 0.456794 | 0.5725425 | 0.5047146 | 0.5336324 | 0.3741919 | 0.4502108 | 0.289591 | 0.3031681 | 0.3394266 | 0.3731273 | 0.31409556 | 0.3560577 | 0.42380953 | 0.54678476 | 0.44414756 | 0.5600052 | 0.49245626 | 0.3647383 | 0.09603187 | 0.30307132 | 0.3503295 | 0.5469388 | 0.3730065 | 0.40479234 | 0.27222848 | 0.3752193 | 0.58393574 | 0.54001206 | 0.40805075 | 0.5173475 |
| 11 | mean_per_class_accuracy | 0.7064223 | 0.067444436 | 0.76335657 | 0.74390244 | 0.72267205 | 0.67291665 | 0.7088435 | 0.7188332 | 0.7735724 | 0.6169355 | 0.65870893 | 0.6663763 | 0.62312925 | 0.7195122 | 0.68541664 | 0.8151343 | 0.7171747 | 0.82138115 | 0.75500315 | 0.7049356 | 0.5419239 | 0.6169028 | 0.6599417 | 0.7734694 | 0.6663265 | 0.77389556 | 0.6265306 | 0.6876097 | 0.79196787 | 0.6914149 | 0.66614 | 0.8087432 |
| 12 | mean_per_class_error | 0.29357764 | 0.067444436 | 0.23664343 | 0.25609756 | 0.27732792 | 0.32708332 | 0.29115647 | 0.2811668 | 0.22642763 | 0.3830645 | 0.34129107 | 0.3336237 | 0.37687075 | 0.2804878 | 0.31458333 | 0.1848657 | 0.28282526 | 0.17861886 | 0.24499688 | 0.2950644 | 0.45807612 | 0.38309717 | 0.34005833 | 0.22653061 | 0.33367348 | 0.22610442 | 0.37346938 | 0.31239036 | 0.20803213 | 0.3085851 | 0.33385998 | 0.19125684 |
| 13 | mse | 0.048665628 | 0.012368429 | 0.04257659 | 0.037540883 | 0.041659515 | 0.06073117 | 0.04770858 | 0.051906154 | 0.037676267 | 0.041295867 | 0.070044294 | 0.04397529 | 0.052012846 | 0.04576735 | 0.06005023 | 0.05337674 | 0.061489433 | 0.024971372 | 0.038327057 | 0.0804319 | 0.057875805 | 0.042564165 | 0.05357335 | 0.034992706 | 0.04665701 | 0.03241043 | 0.048492584 | 0.06057673 | 0.030714849 | 0.06491747 | 0.05378601 | 0.041866194 |
| 14 | null_deviance | 118.046745 | 23.08754 | 109.23703 | 109.23703 | 103.75808 | 142.31174 | 114.7255 | 125.73113 | 81.936905 | 98.28862 | 153.41394 | 109.23703 | 114.7255 | 109.23703 | 142.31174 | 136.7752 | 147.85797 | 81.936905 | 103.63261 | 175.61775 | 120.09978 | 98.16257 | 125.607956 | 109.11213 | 109.11213 | 87.25086 | 109.11213 | 136.65318 | 87.25086 | 158.85994 | 125.607956 | 114.60118 |
| 15 | pr_auc | 0.30016646 | 0.1063161 | 0.2733011 | 0.4730761 | 0.33940038 | 0.3402103 | 0.2206879 | 0.30543798 | 0.09816149 | 0.24624608 | 0.27468687 | 0.31008914 | 0.21960288 | 0.21478876 | 0.42454764 | 0.34266844 | 0.38571241 | 0.3727415 | 0.35758644 | 0.35083106 | 0.0712543 | 0.20643957 | 0.25629458 | 0.56164974 | 0.18617685 | 0.30192536 | 0.169188 | 0.3007012 | 0.32528865 | 0.4740886 | 0.27662897 | 0.3255817 |
| 16 | precision | 0.47172543 | 0.15285723 | 0.42105263 | 0.7 | 0.6 | 0.875 | 0.36842105 | 0.5 | 0.16666667 | 0.42857143 | 0.42105263 | 0.45454547 | 0.44444445 | 0.3181818 | 0.53333336 | 0.5 | 0.5 | 0.5 | 0.5 | 0.38235295 | 0.16666667 | 0.42857143 | 0.42857143 | 0.5714286 | 0.45454547 | 0.31578946 | 0.33333334 | 0.42105263 | 0.6 | 0.8181818 | 0.54545456 | 0.45454547 |
| 17 | r2 | 0.13191184 | 0.08590625 | 0.16429226 | 0.26313484 | 0.12295756 | 0.14470269 | 0.122421786 | 0.15060371 | -0.12745275 | 0.061962195 | 0.09568481 | 0.13683812 | 0.04324673 | 0.10166297 | 0.1542926 | 0.21199656 | 0.17180999 | 0.2527381 | 0.19605462 | 0.109367386 | 0.0014488768 | 0.036691345 | 0.12645747 | 0.31564263 | 0.08752216 | 0.12685774 | 0.05162362 | 0.10887112 | 0.17253704 | 0.19772872 | 0.12298995 | 0.23267043 |
| 18 | recall | 0.44880185 | 0.14306186 | 0.5714286 | 0.5 | 0.46153846 | 0.35 | 0.46666667 | 0.47058824 | 0.6666667 | 0.25 | 0.36363637 | 0.35714287 | 0.26666668 | 0.5 | 0.4 | 0.68421054 | 0.47619048 | 0.6666667 | 0.53846157 | 0.5 | 0.125 | 0.25 | 0.3529412 | 0.5714286 | 0.35714287 | 0.6 | 0.2857143 | 0.42105263 | 0.6 | 0.39130434 | 0.3529412 | 0.6666667 |
| 19 | residual_deviance | 98.03668 | 21.452816 | 85.33435 | 77.65161 | 85.60866 | 120.61124 | 97.02172 | 103.29954 | 79.07346 | 86.144806 | 135.93898 | 92.63428 | 105.896866 | 94.33296 | 118.41398 | 99.772995 | 117.709946 | 55.26006 | 78.659325 | 151.82506 | 119.77048 | 90.65216 | 108.113525 | 75.79646 | 95.45264 | 71.316635 | 97.12428 | 117.17286 | 68.4661 | 123.838036 | 107.31178 | 80.89563 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:13:40 | 0.000 sec | 2 | .81E1 | 15.0 | 0.452218 | 0.451379 | 0.452644 | 0.016166 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:13:40 | 0.004 sec | 4 | .5E1 | 15.0 | 0.450843 | 0.449554 | 0.451331 | 0.016117 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:13:40 | 0.010 sec | 6 | .31E1 | 15.0 | 0.448684 | 0.446685 | 0.449268 | 0.016041 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:13:40 | 0.013 sec | 8 | .19E1 | 15.0 | 0.445333 | 0.442227 | 0.446061 | 0.015925 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:13:40 | 0.016 sec | 10 | .12E1 | 15.0 | 0.440295 | 0.435512 | 0.441228 | 0.015757 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:13:40 | 0.019 sec | 12 | .75E0 | 15.0 | 0.433067 | 0.425847 | 0.434265 | 0.01553 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:13:40 | 0.021 sec | 14 | .46E0 | 15.0 | 0.4235 | 0.412991 | 0.424984 | 0.015261 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:13:40 | 0.024 sec | 16 | .29E0 | 15.0 | 0.412412 | 0.397969 | 0.414118 | 0.015007 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:13:40 | 0.027 sec | 18 | .18E0 | 15.0 | 0.401653 | 0.3832 | 0.403463 | 0.014845 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:13:40 | 0.029 sec | 20 | .11E0 | 15.0 | 0.392994 | 0.371097 | 0.394844 | 0.014811 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:13:40 | 0.032 sec | 22 | .69E-1 | 15.0 | 0.386978 | 0.362493 | 0.388898 | 0.014875 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:13:40 | 0.034 sec | 24 | .43E-1 | 15.0 | 0.383167 | 0.356908 | 0.38471 | 0.014881 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:13:40 | 0.037 sec | 26 | .27E-1 | 15.0 | 0.380855 | 0.353435 | 0.382432 | 0.014954 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:13:40 | 0.039 sec | 28 | .17E-1 | 15.0 | 0.379469 | 0.351288 | 0.381126 | 0.015013 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:13:40 | 0.328 sec | 29 | None | NaN | 29.0 | 0.220895 | 0.189323 | 0.136301 | 0.762771 | 0.28025 | 8.531668 | 0.074364 | 0.213965 | 0.174978 | 0.189448 | 0.810677 | 0.329901 | 8.320513 | 0.07396 | ||||||
| 15 | 2021-07-15 20:13:40 | 0.042 sec | 30 | .1E-1 | 15.0 | 0.378646 | 0.349956 | 0.380406 | 0.015055 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:13:40 | 0.045 sec | 32 | .64E-2 | 15.0 | 0.378169 | 0.349122 | 0.380857 | 0.015174 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:13:40 | 0.047 sec | 34 | .4E-2 | 15.0 | 0.377902 | 0.348594 | 0.380673 | 0.01519 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:13:40 | 0.049 sec | 35 | .25E-2 | 15.0 | 0.377763 | 0.348266 | 0.384017 | 0.016071 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.526708 | 1.000000 | 0.266819 |
| 1 | Average_Transaction_Frequency | 0.253913 | 0.482075 | 0.128627 |
| 2 | Minimum_Transaction_Amount | 0.194835 | 0.369910 | 0.098699 |
| 3 | Merchant_ID | 0.191137 | 0.362889 | 0.096826 |
| 4 | Channel_ID | 0.154796 | 0.293894 | 0.078416 |
| 5 | Card_Type.1 | 0.134619 | 0.255585 | 0.068195 |
| 6 | Card_Type.0 | 0.133193 | 0.252878 | 0.067473 |
| 7 | Transaction_Amount | 0.089772 | 0.170439 | 0.045476 |
| 8 | Transaction_Date | 0.073042 | 0.138677 | 0.037002 |
| 9 | Average_Transaction_Amount | 0.067222 | 0.127627 | 0.034053 |
| 10 | Day | 0.057172 | 0.108546 | 0.028962 |
| 11 | Month | 0.052593 | 0.099851 | 0.026642 |
| 12 | Maximum_Transaction_Amount | 0.041366 | 0.078537 | 0.020955 |
| 13 | City_ID | 0.003661 | 0.006951 | 0.001855 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201342 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01143 ) | nlambda = 30, lambda.max = 9.0074, lambda.min = 0.01143, lambda.1s... | 14 | 14 | 30 | automl_training_py_435_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.047234398250875984 RMSE: 0.2173347607974297 LogLoss: 0.18256447062873388 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793838 Residual deviance: 2842.893936630644 AIC: 2872.893936630644 AUC: 0.7867303546109231 AUCPR: 0.30708802257212003 Gini: 0.5734607092218462 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.25718870911651387:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7015.0 | 303.0 | 0.0414 | (303.0/7318.0) |
| 1 | 1 | 257.0 | 211.0 | 0.5491 | (257.0/468.0) |
| 2 | Total | 7272.0 | 514.0 | 0.0719 | (560.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.257189 | 0.429735 | 159.0 |
| 1 | max f2 | 0.071000 | 0.458591 | 225.0 |
| 2 | max f0point5 | 0.319426 | 0.434680 | 126.0 |
| 3 | max accuracy | 0.575282 | 0.940920 | 12.0 |
| 4 | max precision | 0.848811 | 1.000000 | 0.0 |
| 5 | max recall | 0.019492 | 1.000000 | 378.0 |
| 6 | max specificity | 0.848811 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.257189 | 0.391933 | 159.0 |
| 8 | max min_per_class_accuracy | 0.041077 | 0.709402 | 288.0 |
| 9 | max mean_per_class_accuracy | 0.059475 | 0.724596 | 241.0 |
| 10 | max tns | 0.848811 | 7318.000000 | 0.0 |
| 11 | max fns | 0.848811 | 467.000000 | 0.0 |
| 12 | max fps | 0.001199 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019492 | 468.000000 | 378.0 |
| 14 | max tnr | 0.848811 | 1.000000 | 0.0 |
| 15 | max fnr | 0.848811 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001199 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019492 | 1.000000 | 378.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.435956 | 8.744959 | 8.744959 | 0.525641 | 0.518587 | 0.525641 | 0.518587 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.398500 | 7.465209 | 8.105084 | 0.448718 | 0.415337 | 0.487179 | 0.466962 | 0.074786 | 0.162393 | 646.520929 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.371748 | 8.318376 | 8.176182 | 0.500000 | 0.384393 | 0.491453 | 0.439439 | 0.083333 | 0.245726 | 731.837607 | 717.618161 | 0.229465 |
| 3 | 4 | 0.040072 | 0.352166 | 5.972167 | 7.625178 | 0.358974 | 0.362437 | 0.458333 | 0.420189 | 0.059829 | 0.305556 | 497.216743 | 662.517806 | 0.282462 |
| 4 | 5 | 0.050090 | 0.329889 | 6.398751 | 7.379893 | 0.384615 | 0.342310 | 0.443590 | 0.404613 | 0.064103 | 0.369658 | 539.875082 | 637.989261 | 0.340005 |
| 5 | 6 | 0.100051 | 0.064436 | 2.908224 | 5.146928 | 0.174807 | 0.168480 | 0.309371 | 0.286698 | 0.145299 | 0.514957 | 190.822402 | 414.692845 | 0.441440 |
| 6 | 7 | 0.150013 | 0.050900 | 0.940896 | 3.746118 | 0.056555 | 0.056004 | 0.225171 | 0.209866 | 0.047009 | 0.561966 | -5.910399 | 274.611799 | 0.438298 |
| 7 | 8 | 0.200103 | 0.046366 | 0.682533 | 2.979239 | 0.041026 | 0.048367 | 0.179076 | 0.169439 | 0.034188 | 0.596154 | -31.746658 | 197.923867 | 0.421379 |
| 8 | 9 | 0.300026 | 0.041362 | 1.047816 | 2.335982 | 0.062982 | 0.043607 | 0.140411 | 0.127531 | 0.104701 | 0.700855 | 4.781601 | 133.598232 | 0.426463 |
| 9 | 10 | 0.400077 | 0.038307 | 0.427131 | 1.858616 | 0.025674 | 0.039777 | 0.111717 | 0.105586 | 0.042735 | 0.743590 | -57.286901 | 85.861629 | 0.365481 |
| 10 | 11 | 0.500000 | 0.035633 | 0.620136 | 1.611111 | 0.037275 | 0.036929 | 0.096840 | 0.091865 | 0.061966 | 0.805556 | -37.986399 | 61.111111 | 0.325096 |
| 11 | 12 | 0.600051 | 0.033338 | 0.811549 | 1.477794 | 0.048780 | 0.034500 | 0.088827 | 0.082300 | 0.081197 | 0.886752 | -18.845112 | 47.779369 | 0.305036 |
| 12 | 13 | 0.699974 | 0.030976 | 0.363528 | 1.318730 | 0.021851 | 0.032142 | 0.079266 | 0.075140 | 0.036325 | 0.923077 | -63.647200 | 31.872971 | 0.237370 |
| 13 | 14 | 0.800026 | 0.028295 | 0.234922 | 1.183189 | 0.014121 | 0.029715 | 0.071119 | 0.069459 | 0.023504 | 0.946581 | -76.507795 | 18.318850 | 0.155928 |
| 14 | 15 | 0.899949 | 0.024126 | 0.299376 | 1.085057 | 0.017995 | 0.026400 | 0.065220 | 0.064678 | 0.029915 | 0.976496 | -70.062400 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.000901 | 0.234922 | 1.000000 | 0.014121 | 0.019001 | 0.060108 | 0.060108 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.05174559459329074 RMSE: 0.2274765803182621 LogLoss: 0.20046419277069702 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311477 Residual deviance: 780.6075666490945 AIC: 810.6075666490945 AUC: 0.7230068656298165 AUCPR: 0.21573027301431189 Gini: 0.44601373125963306 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.10821106546255815:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1731.0 | 99.0 | 0.0541 | (99.0/1830.0) |
| 1 | 1 | 72.0 | 45.0 | 0.6154 | (72.0/117.0) |
| 2 | Total | 1803.0 | 144.0 | 0.0878 | (171.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.108211 | 0.344828 | 119.0 |
| 1 | max f2 | 0.051208 | 0.386179 | 183.0 |
| 2 | max f0point5 | 0.349514 | 0.341176 | 61.0 |
| 3 | max accuracy | 0.462992 | 0.940421 | 7.0 |
| 4 | max precision | 0.462992 | 0.555556 | 7.0 |
| 5 | max recall | 0.018903 | 1.000000 | 370.0 |
| 6 | max specificity | 0.757876 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.108211 | 0.300146 | 119.0 |
| 8 | max min_per_class_accuracy | 0.039013 | 0.634426 | 246.0 |
| 9 | max mean_per_class_accuracy | 0.051208 | 0.685393 | 183.0 |
| 10 | max tns | 0.757876 | 1829.000000 | 0.0 |
| 11 | max fns | 0.757876 | 117.000000 | 0.0 |
| 12 | max fps | 0.001162 | 1830.000000 | 399.0 |
| 13 | max tps | 0.018903 | 117.000000 | 370.0 |
| 14 | max tnr | 0.757876 | 0.999454 | 0.0 |
| 15 | max fnr | 0.757876 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001162 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018903 | 1.000000 | 370.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.86 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.432101 | 6.656410 | 6.656410 | 0.400000 | 0.481105 | 0.400000 | 0.481105 | 0.068376 | 0.068376 | 565.641026 | 565.641026 | 0.061819 |
| 1 | 2 | 0.020031 | 0.400659 | 6.130904 | 6.400394 | 0.368421 | 0.413748 | 0.384615 | 0.448290 | 0.059829 | 0.128205 | 513.090418 | 540.039448 | 0.115090 |
| 2 | 3 | 0.030303 | 0.364571 | 4.992308 | 5.923077 | 0.300000 | 0.382313 | 0.355932 | 0.425925 | 0.051282 | 0.179487 | 399.230769 | 492.307692 | 0.158722 |
| 3 | 4 | 0.040062 | 0.347895 | 7.006748 | 6.187048 | 0.421053 | 0.356836 | 0.371795 | 0.409096 | 0.068376 | 0.247863 | 600.674764 | 518.704799 | 0.221087 |
| 4 | 5 | 0.050334 | 0.319323 | 2.496154 | 5.433804 | 0.150000 | 0.333674 | 0.326531 | 0.393704 | 0.025641 | 0.273504 | 149.615385 | 443.380429 | 0.237439 |
| 5 | 6 | 0.100154 | 0.063149 | 3.088025 | 4.266930 | 0.185567 | 0.151655 | 0.256410 | 0.273300 | 0.153846 | 0.427350 | 208.802538 | 326.692965 | 0.348115 |
| 6 | 7 | 0.149974 | 0.049634 | 1.372456 | 3.305409 | 0.082474 | 0.055010 | 0.198630 | 0.200786 | 0.068376 | 0.495726 | 37.245572 | 230.540920 | 0.367858 |
| 7 | 8 | 0.200308 | 0.045594 | 0.339613 | 2.560158 | 0.020408 | 0.047452 | 0.153846 | 0.162256 | 0.017094 | 0.512821 | -66.038723 | 156.015779 | 0.332493 |
| 8 | 9 | 0.299949 | 0.041601 | 0.514671 | 1.880664 | 0.030928 | 0.043370 | 0.113014 | 0.122763 | 0.051282 | 0.564103 | -48.532910 | 88.066386 | 0.281042 |
| 9 | 10 | 0.400103 | 0.038428 | 1.024063 | 1.666239 | 0.061538 | 0.040077 | 0.100128 | 0.102065 | 0.102564 | 0.666667 | 2.406312 | 66.623877 | 0.283607 |
| 10 | 11 | 0.500257 | 0.035754 | 0.938725 | 1.520587 | 0.056410 | 0.037054 | 0.091376 | 0.089049 | 0.094017 | 0.760684 | -6.127548 | 52.058653 | 0.277077 |
| 11 | 12 | 0.599897 | 0.033527 | 0.772006 | 1.396250 | 0.046392 | 0.034600 | 0.083904 | 0.080006 | 0.076923 | 0.837607 | -22.799366 | 39.625044 | 0.252907 |
| 12 | 13 | 0.700051 | 0.030702 | 0.426693 | 1.257539 | 0.025641 | 0.032199 | 0.075569 | 0.073166 | 0.042735 | 0.880342 | -57.330703 | 25.753899 | 0.191817 |
| 13 | 14 | 0.799692 | 0.028022 | 0.514671 | 1.164979 | 0.030928 | 0.029394 | 0.070006 | 0.067712 | 0.051282 | 0.931624 | -48.532910 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.023427 | 0.512032 | 1.092305 | 0.030769 | 0.025899 | 0.065639 | 0.063058 | 0.051282 | 0.982906 | -48.796844 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.001039 | 0.170677 | 1.000000 | 0.010256 | 0.018398 | 0.060092 | 0.058585 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04756421118050309 RMSE: 0.21809220797750453 LogLoss: 0.18387896465723003 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.694542627468 Residual deviance: 2863.363237642386 AIC: 2893.363237642386 AUC: 0.7774409721492258 AUCPR: 0.2926718210414641 Gini: 0.5548819442984516 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.27238847360380153:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7024.0 | 294.0 | 0.0402 | (294.0/7318.0) |
| 1 | 1 | 262.0 | 206.0 | 0.5598 | (262.0/468.0) |
| 2 | Total | 7286.0 | 500.0 | 0.0714 | (556.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.272388 | 0.425620 | 151.0 |
| 1 | max f2 | 0.069286 | 0.453846 | 222.0 |
| 2 | max f0point5 | 0.308564 | 0.422630 | 133.0 |
| 3 | max accuracy | 0.666549 | 0.940277 | 6.0 |
| 4 | max precision | 0.666549 | 0.714286 | 6.0 |
| 5 | max recall | 0.018733 | 1.000000 | 380.0 |
| 6 | max specificity | 0.855594 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.272388 | 0.387833 | 151.0 |
| 8 | max min_per_class_accuracy | 0.040965 | 0.702991 | 287.0 |
| 9 | max mean_per_class_accuracy | 0.059827 | 0.720733 | 234.0 |
| 10 | max tns | 0.855594 | 7317.000000 | 0.0 |
| 11 | max fns | 0.855594 | 468.000000 | 0.0 |
| 12 | max fps | 0.001293 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018733 | 468.000000 | 380.0 |
| 14 | max tnr | 0.855594 | 0.999863 | 0.0 |
| 15 | max fnr | 0.855594 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001293 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018733 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.438826 | 8.531668 | 8.531668 | 0.512821 | 0.521087 | 0.512821 | 0.521087 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.397081 | 6.825334 | 7.678501 | 0.410256 | 0.414490 | 0.461538 | 0.467788 | 0.068376 | 0.153846 | 582.533421 | 667.850099 | 0.142368 |
| 2 | 3 | 0.030054 | 0.370307 | 8.531668 | 7.962890 | 0.512821 | 0.383703 | 0.478632 | 0.439760 | 0.085470 | 0.239316 | 753.166776 | 696.288991 | 0.222645 |
| 3 | 4 | 0.040072 | 0.352821 | 5.332292 | 7.305241 | 0.320513 | 0.361444 | 0.439103 | 0.420181 | 0.053419 | 0.292735 | 433.229235 | 630.524052 | 0.268821 |
| 4 | 5 | 0.050090 | 0.329969 | 7.038626 | 7.251918 | 0.423077 | 0.341649 | 0.435897 | 0.404475 | 0.070513 | 0.363248 | 603.862590 | 625.191760 | 0.333185 |
| 5 | 6 | 0.100051 | 0.064244 | 2.908224 | 5.082859 | 0.174807 | 0.168113 | 0.305520 | 0.286445 | 0.145299 | 0.508547 | 190.822402 | 408.285880 | 0.434620 |
| 6 | 7 | 0.150013 | 0.051131 | 0.898128 | 3.689143 | 0.053985 | 0.056071 | 0.221747 | 0.209720 | 0.044872 | 0.553419 | -10.187199 | 268.914281 | 0.429205 |
| 7 | 8 | 0.200103 | 0.046299 | 0.853167 | 2.979239 | 0.051282 | 0.048397 | 0.179076 | 0.169337 | 0.042735 | 0.596154 | -14.683322 | 197.923867 | 0.421379 |
| 8 | 9 | 0.300026 | 0.041424 | 0.876744 | 2.279007 | 0.052699 | 0.043611 | 0.136986 | 0.127464 | 0.087607 | 0.683761 | -12.325599 | 127.900714 | 0.408276 |
| 9 | 10 | 0.400077 | 0.038338 | 0.491201 | 1.831912 | 0.029525 | 0.039814 | 0.110112 | 0.105545 | 0.049145 | 0.732906 | -50.879936 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.035767 | 0.555984 | 1.576923 | 0.033419 | 0.037023 | 0.094786 | 0.091851 | 0.055556 | 0.788462 | -44.401600 | 57.692308 | 0.306909 |
| 11 | 12 | 0.600051 | 0.033416 | 0.875619 | 1.459989 | 0.052632 | 0.034569 | 0.087757 | 0.082300 | 0.087607 | 0.876068 | -12.438147 | 45.998895 | 0.293669 |
| 12 | 13 | 0.699974 | 0.031084 | 0.406296 | 1.309572 | 0.024422 | 0.032190 | 0.078716 | 0.075147 | 0.040598 | 0.916667 | -59.370400 | 30.957187 | 0.230550 |
| 13 | 14 | 0.800026 | 0.028341 | 0.277635 | 1.180518 | 0.016688 | 0.029773 | 0.070958 | 0.069472 | 0.027778 | 0.944444 | -72.236486 | 18.051765 | 0.153655 |
| 14 | 15 | 0.899949 | 0.024213 | 0.299376 | 1.082683 | 0.017995 | 0.026512 | 0.065078 | 0.064702 | 0.029915 | 0.974359 | -70.062400 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.000967 | 0.256279 | 1.000000 | 0.015404 | 0.019129 | 0.060108 | 0.060143 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9356435 | 0.020894697 | 0.95 | 0.95 | 0.9576923 | 0.95384616 | 0.9423077 | 0.96153843 | 0.9461538 | 0.95 | 0.89615387 | 0.9 | 0.9307692 | 0.9269231 | 0.9307692 | 0.9423077 | 0.9346154 | 0.97692305 | 0.9498069 | 0.93436295 | 0.93050194 | 0.9189189 | 0.9150579 | 0.93436295 | 0.96138996 | 0.9227799 | 0.94208497 | 0.93436295 | 0.96138996 | 0.8918919 | 0.9034749 | 0.9189189 |
| 1 | auc | 0.7828334 | 0.06767657 | 0.77006805 | 0.80940515 | 0.8276743 | 0.81741494 | 0.63821137 | 0.92906743 | 0.8209366 | 0.85209394 | 0.6652149 | 0.7368421 | 0.84652627 | 0.7837094 | 0.7588962 | 0.7775482 | 0.7479508 | 0.8644 | 0.8283302 | 0.76678175 | 0.8701993 | 0.81776315 | 0.7927114 | 0.8078704 | 0.8531842 | 0.70193636 | 0.72947216 | 0.7482456 | 0.67198247 | 0.6822404 | 0.8147296 | 0.753596 |
| 2 | err | 0.0643565 | 0.020894697 | 0.05 | 0.05 | 0.042307694 | 0.046153847 | 0.057692308 | 0.03846154 | 0.053846154 | 0.05 | 0.103846155 | 0.1 | 0.06923077 | 0.073076926 | 0.06923077 | 0.057692308 | 0.06538462 | 0.023076924 | 0.05019305 | 0.06563707 | 0.06949807 | 0.08108108 | 0.08494209 | 0.06563707 | 0.038610037 | 0.077220075 | 0.057915058 | 0.06563707 | 0.038610037 | 0.10810811 | 0.096525095 | 0.08108108 |
| 3 | err_count | 16.7 | 5.4148583 | 13.0 | 13.0 | 11.0 | 12.0 | 15.0 | 10.0 | 14.0 | 13.0 | 27.0 | 26.0 | 18.0 | 19.0 | 18.0 | 15.0 | 17.0 | 6.0 | 13.0 | 17.0 | 18.0 | 21.0 | 22.0 | 17.0 | 10.0 | 20.0 | 15.0 | 17.0 | 10.0 | 28.0 | 25.0 | 21.0 |
| 4 | f0point5 | 0.49133003 | 0.12813506 | 0.54545456 | 0.5072464 | 0.49019608 | 0.6 | 0.43103448 | 0.46875 | 0.6097561 | 0.61728394 | 0.28688523 | 0.23809524 | 0.5045872 | 0.5825243 | 0.47058824 | 0.5813953 | 0.44117647 | 0.7692308 | 0.5072464 | 0.6097561 | 0.51282054 | 0.45454547 | 0.38135594 | 0.48913044 | 0.73770493 | 0.44444445 | 0.37313432 | 0.49019608 | 0.6060606 | 0.23364486 | 0.46153846 | 0.29411766 |
| 5 | f1 | 0.48010138 | 0.098569386 | 0.48 | 0.5185185 | 0.47619048 | 0.6 | 0.4 | 0.54545456 | 0.5882353 | 0.6060606 | 0.34146342 | 0.2777778 | 0.55 | 0.55813956 | 0.47058824 | 0.5714286 | 0.41379312 | 0.5714286 | 0.5185185 | 0.5405405 | 0.5714286 | 0.46153846 | 0.45 | 0.51428574 | 0.64285713 | 0.44444445 | 0.4 | 0.37037036 | 0.44444445 | 0.2631579 | 0.48979592 | 0.32258064 |
| 6 | f2 | 0.48255068 | 0.0999778 | 0.42857143 | 0.530303 | 0.46296296 | 0.6 | 0.37313432 | 0.65217394 | 0.5681818 | 0.5952381 | 0.42168674 | 0.33333334 | 0.6043956 | 0.53571427 | 0.47058824 | 0.56179774 | 0.38961038 | 0.45454547 | 0.530303 | 0.4854369 | 0.6451613 | 0.46875 | 0.5487805 | 0.5421687 | 0.56962025 | 0.44444445 | 0.43103448 | 0.29761904 | 0.3508772 | 0.30120483 | 0.5217391 | 0.35714287 |
| 7 | lift_top_group | 9.547556 | 5.421819 | 11.555555 | 13.333333 | 7.878788 | 5.7777777 | 12.380953 | 10.833333 | 4.814815 | 5.098039 | 6.1904764 | 6.6666665 | 10.196078 | 11.304348 | 15.294118 | 9.62963 | 10.833333 | 26.0 | 13.282051 | 11.772727 | 10.156863 | 4.5438595 | 12.333333 | 5.3958335 | 15.235294 | 0.0 | 7.848485 | 4.5438595 | 19.923077 | 5.7555556 | 7.848485 | 0.0 |
| 8 | logloss | 0.18189177 | 0.031985044 | 0.17678645 | 0.15202254 | 0.1538465 | 0.15635118 | 0.18631846 | 0.10472913 | 0.1859423 | 0.17034452 | 0.19489628 | 0.18515496 | 0.17398775 | 0.2335094 | 0.19242714 | 0.19237784 | 0.19481798 | 0.122229666 | 0.14823058 | 0.22986843 | 0.16923635 | 0.21340278 | 0.17042741 | 0.18070477 | 0.16099475 | 0.2134372 | 0.15414324 | 0.23859963 | 0.17107654 | 0.20772956 | 0.24028394 | 0.18287595 |
| 9 | max_per_class_error | 0.5086972 | 0.116446905 | 0.6 | 0.46153846 | 0.54545456 | 0.4 | 0.64285713 | 0.25 | 0.44444445 | 0.4117647 | 0.5 | 0.61538464 | 0.3529412 | 0.47826087 | 0.5294118 | 0.44444445 | 0.625 | 0.6 | 0.46153846 | 0.54545456 | 0.29411766 | 0.5263158 | 0.35714287 | 0.4375 | 0.47058824 | 0.5555556 | 0.54545456 | 0.7368421 | 0.6923077 | 0.6666667 | 0.45454547 | 0.61538464 |
| 10 | mcc | 0.45741603 | 0.106799126 | 0.46517935 | 0.49256343 | 0.4547586 | 0.5755102 | 0.3731273 | 0.5495304 | 0.56064636 | 0.57969624 | 0.3097946 | 0.23926316 | 0.5203019 | 0.52002347 | 0.4335512 | 0.54078156 | 0.38185778 | 0.625 | 0.49245626 | 0.5173475 | 0.5469034 | 0.41788897 | 0.4315118 | 0.48140234 | 0.639986 | 0.40295067 | 0.3730065 | 0.3777304 | 0.4818126 | 0.2131391 | 0.43989992 | 0.28485996 |
| 11 | mean_per_class_accuracy | 0.7276683 | 0.057032283 | 0.6918367 | 0.75506073 | 0.7172326 | 0.78775513 | 0.6663763 | 0.859127 | 0.7653811 | 0.78177196 | 0.7093496 | 0.65587044 | 0.7988381 | 0.7439919 | 0.7167756 | 0.76331496 | 0.6731557 | 0.7 | 0.75500315 | 0.7167242 | 0.8260817 | 0.7139254 | 0.7867347 | 0.7606739 | 0.7605736 | 0.7014753 | 0.70912755 | 0.62532896 | 0.6518136 | 0.6297814 | 0.7410817 | 0.6658849 |
| 12 | mean_per_class_error | 0.27233174 | 0.057032283 | 0.30816326 | 0.24493927 | 0.28276744 | 0.2122449 | 0.3336237 | 0.14087301 | 0.23461892 | 0.21822803 | 0.2906504 | 0.34412956 | 0.20116195 | 0.25600806 | 0.2832244 | 0.23668504 | 0.32684427 | 0.3 | 0.24499688 | 0.28327578 | 0.17391832 | 0.28607455 | 0.2132653 | 0.23932613 | 0.23942634 | 0.29852468 | 0.29087242 | 0.37467104 | 0.34818637 | 0.37021858 | 0.2589183 | 0.33411506 |
| 13 | mse | 0.04683702 | 0.009589695 | 0.04436419 | 0.03762829 | 0.037391298 | 0.039763678 | 0.045659427 | 0.024342172 | 0.049655106 | 0.046393 | 0.048833832 | 0.046231266 | 0.046486735 | 0.06264155 | 0.049637243 | 0.050865564 | 0.04969468 | 0.028476773 | 0.03718026 | 0.06083934 | 0.04546587 | 0.05774174 | 0.044335924 | 0.0471759 | 0.04165074 | 0.056572217 | 0.038095262 | 0.0633612 | 0.04096349 | 0.052547034 | 0.06532441 | 0.045792446 |
| 14 | null_deviance | 118.023155 | 19.403027 | 114.7255 | 103.75808 | 92.82863 | 114.7255 | 109.23703 | 76.50513 | 131.24835 | 125.73113 | 109.23703 | 103.75808 | 125.73113 | 158.97968 | 125.73113 | 131.24835 | 120.223526 | 87.37807 | 103.63261 | 153.29364 | 125.607956 | 136.65318 | 109.11213 | 120.09978 | 125.607956 | 131.12575 | 92.701996 | 136.65318 | 103.63261 | 114.60118 | 153.29364 | 103.63261 |
| 15 | pr_auc | 0.34350485 | 0.11960377 | 0.3727678 | 0.3650462 | 0.24925463 | 0.3977182 | 0.2666194 | 0.34285817 | 0.3750678 | 0.35608003 | 0.14352624 | 0.14255257 | 0.42465603 | 0.51864266 | 0.43653122 | 0.4412156 | 0.3111897 | 0.50113267 | 0.4089728 | 0.4696987 | 0.47360638 | 0.3128144 | 0.33884585 | 0.34127808 | 0.5967499 | 0.22411917 | 0.23516928 | 0.23998782 | 0.36657405 | 0.12360924 | 0.39205855 | 0.1368023 |
| 16 | precision | 0.5103093 | 0.17433529 | 0.6 | 0.5 | 0.5 | 0.6 | 0.45454547 | 0.42857143 | 0.625 | 0.625 | 0.25925925 | 0.2173913 | 0.47826087 | 0.6 | 0.47058824 | 0.5882353 | 0.46153846 | 1.0 | 0.5 | 0.6666667 | 0.48 | 0.45 | 0.34615386 | 0.47368422 | 0.8181818 | 0.44444445 | 0.35714287 | 0.625 | 0.8 | 0.2173913 | 0.44444445 | 0.2777778 |
| 17 | r2 | 0.16377448 | 0.07878514 | 0.18394034 | 0.2078255 | 0.0771626 | 0.2685647 | 0.10378133 | 0.18376446 | 0.22941113 | 0.2408214 | 0.041472975 | 0.026710173 | 0.23928753 | 0.22315739 | 0.18773237 | 0.21062623 | 0.13950808 | 0.22998808 | 0.22010978 | 0.21726818 | 0.2586543 | 0.15057595 | 0.13291602 | 0.18605803 | 0.32086214 | 0.12519115 | 0.06324473 | 0.067909546 | 0.140753 | 0.036910497 | 0.15956528 | 0.03946121 |
| 18 | recall | 0.4913028 | 0.116446905 | 0.4 | 0.53846157 | 0.45454547 | 0.6 | 0.35714287 | 0.75 | 0.5555556 | 0.5882353 | 0.5 | 0.3846154 | 0.64705884 | 0.5217391 | 0.47058824 | 0.5555556 | 0.375 | 0.4 | 0.53846157 | 0.45454547 | 0.7058824 | 0.47368422 | 0.64285713 | 0.5625 | 0.5294118 | 0.44444445 | 0.45454547 | 0.2631579 | 0.30769232 | 0.33333334 | 0.54545456 | 0.3846154 |
| 19 | residual_deviance | 94.40499 | 16.549541 | 91.928955 | 79.05173 | 80.00018 | 81.30261 | 96.8856 | 54.45915 | 96.689995 | 88.57915 | 101.34606 | 96.28058 | 90.473625 | 121.4249 | 100.06212 | 100.036476 | 101.30535 | 63.559425 | 76.78344 | 119.071846 | 87.66443 | 110.54263 | 88.2814 | 93.60507 | 83.39528 | 110.56047 | 79.8462 | 123.59461 | 88.617645 | 107.60391 | 124.46708 | 94.729744 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:13:50 | 0.000 sec | 2 | .9E1 | 15.0 | 0.451983 | 0.452569 | 0.452347 | 0.013586 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:13:50 | 0.003 sec | 4 | .56E1 | 15.0 | 0.450464 | 0.451453 | 0.450893 | 0.013529 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:13:50 | 0.005 sec | 6 | .35E1 | 15.0 | 0.448075 | 0.449701 | 0.448605 | 0.01344 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:13:50 | 0.007 sec | 8 | .22E1 | 15.0 | 0.444356 | 0.446982 | 0.445037 | 0.013303 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:13:50 | 0.010 sec | 10 | .13E1 | 15.0 | 0.438732 | 0.442893 | 0.439631 | 0.013099 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:13:50 | 0.013 sec | 12 | .83E0 | 15.0 | 0.430591 | 0.437032 | 0.431776 | 0.01281 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:13:50 | 0.015 sec | 14 | .52E0 | 15.0 | 0.419662 | 0.429298 | 0.421162 | 0.012439 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:13:51 | 0.018 sec | 16 | .32E0 | 15.0 | 0.406732 | 0.42043 | 0.408485 | 0.012032 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:13:51 | 0.020 sec | 18 | .2E0 | 15.0 | 0.39388 | 0.412101 | 0.395748 | 0.011678 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:13:51 | 0.023 sec | 20 | .12E0 | 15.0 | 0.383301 | 0.405908 | 0.385202 | 0.011456 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:13:51 | 0.025 sec | 22 | .77E-1 | 15.0 | 0.375835 | 0.402266 | 0.377785 | 0.011375 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:13:51 | 0.028 sec | 24 | .48E-1 | 15.0 | 0.371037 | 0.400613 | 0.373111 | 0.011392 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:13:51 | 0.030 sec | 26 | .3E-1 | 15.0 | 0.368079 | 0.400183 | 0.370332 | 0.01146 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:13:51 | 0.033 sec | 28 | .18E-1 | 15.0 | 0.366255 | 0.400403 | 0.368717 | 0.011548 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:13:50 | 0.268 sec | 29 | None | NaN | 29.0 | 0.217335 | 0.182564 | 0.163918 | 0.78673 | 0.307088 | 8.744959 | 0.071924 | 0.227477 | 0.200464 | 0.083846 | 0.723007 | 0.21573 | 6.65641 | 0.087827 | ||||||
| 15 | 2021-07-15 20:13:51 | 0.035 sec | 30 | .11E-1 | 15.0 | 0.365129 | 0.400928 | 0.367792 | 0.011635 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:13:51 | 0.038 sec | 32 | .71E-2 | 15.0 | 0.364441 | 0.401562 | 0.368661 | 0.011859 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:13:51 | 0.040 sec | 34 | .44E-2 | 15.0 | 0.364037 | 0.402184 | 0.370524 | 0.012574 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:13:51 | 0.043 sec | 36 | .27E-2 | 15.0 | 0.363814 | 0.402729 | 0.375619 | 0.012601 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.561494 | 1.000000 | 0.267501 |
| 1 | Average_Transaction_Frequency | 0.265071 | 0.472082 | 0.126282 |
| 2 | Channel_ID | 0.187680 | 0.334252 | 0.089413 |
| 3 | Merchant_ID | 0.187507 | 0.333943 | 0.089330 |
| 4 | Minimum_Transaction_Amount | 0.181662 | 0.323533 | 0.086545 |
| 5 | Card_Type.1 | 0.159541 | 0.284137 | 0.076007 |
| 6 | Card_Type.0 | 0.158098 | 0.281566 | 0.075319 |
| 7 | Transaction_Amount | 0.111593 | 0.198743 | 0.053164 |
| 8 | Average_Transaction_Amount | 0.072055 | 0.128328 | 0.034328 |
| 9 | Transaction_Date | 0.068509 | 0.122012 | 0.032638 |
| 10 | Month | 0.049138 | 0.087514 | 0.023410 |
| 11 | Day | 0.042280 | 0.075300 | 0.020143 |
| 12 | Maximum_Transaction_Amount | 0.030074 | 0.053561 | 0.014328 |
| 13 | City_ID | 0.024336 | 0.043342 | 0.011594 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201354 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01065 ) | nlambda = 30, lambda.max = 8.3917, lambda.min = 0.01065, lambda.1s... | 14 | 14 | 30 | automl_training_py_470_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04831720948050228 RMSE: 0.21981175919523113 LogLoss: 0.1865826636738435 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793834 Residual deviance: 2905.4652387290907 AIC: 2935.4652387290907 AUC: 0.7761817833558747 AUCPR: 0.28643206371928964 Gini: 0.5523635667117495 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.28695166766118196:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7042.0 | 276.0 | 0.0377 | (276.0/7318.0) |
| 1 | 1 | 277.0 | 191.0 | 0.5919 | (277.0/468.0) |
| 2 | Total | 7319.0 | 467.0 | 0.071 | (553.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.286952 | 0.408556 | 135.0 |
| 1 | max f2 | 0.085615 | 0.443962 | 208.0 |
| 2 | max f0point5 | 0.306693 | 0.416667 | 123.0 |
| 3 | max accuracy | 0.416743 | 0.940406 | 45.0 |
| 4 | max precision | 0.870589 | 1.000000 | 0.0 |
| 5 | max recall | 0.019585 | 1.000000 | 381.0 |
| 6 | max specificity | 0.870589 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.286952 | 0.370775 | 135.0 |
| 8 | max min_per_class_accuracy | 0.041626 | 0.696581 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.061441 | 0.715527 | 232.0 |
| 10 | max tns | 0.870589 | 7318.000000 | 0.0 |
| 11 | max fns | 0.870589 | 467.000000 | 0.0 |
| 12 | max fps | 0.001870 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019585 | 468.000000 | 381.0 |
| 14 | max tnr | 0.870589 | 1.000000 | 0.0 |
| 15 | max fnr | 0.870589 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001870 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019585 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.410882 | 8.744959 | 8.744959 | 0.525641 | 0.498119 | 0.525641 | 0.498119 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.376441 | 7.465209 | 8.105084 | 0.448718 | 0.392347 | 0.487179 | 0.445233 | 0.074786 | 0.162393 | 646.520929 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.352577 | 6.398751 | 7.536307 | 0.384615 | 0.364300 | 0.452991 | 0.418255 | 0.064103 | 0.226496 | 539.875082 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.334962 | 6.612043 | 7.305241 | 0.397436 | 0.342734 | 0.439103 | 0.399375 | 0.066239 | 0.292735 | 561.204252 | 630.524052 | 0.268821 |
| 4 | 5 | 0.050090 | 0.312813 | 6.185459 | 7.081284 | 0.371795 | 0.324652 | 0.425641 | 0.384430 | 0.061966 | 0.354701 | 518.545913 | 608.128424 | 0.324091 |
| 5 | 6 | 0.100051 | 0.067031 | 2.950992 | 5.018789 | 0.177378 | 0.170149 | 0.301669 | 0.277427 | 0.147436 | 0.502137 | 195.099202 | 401.878916 | 0.427800 |
| 6 | 7 | 0.150013 | 0.051774 | 0.812592 | 3.617924 | 0.048843 | 0.057266 | 0.217466 | 0.204103 | 0.040598 | 0.542735 | -18.740799 | 261.792384 | 0.417838 |
| 7 | 8 | 0.200103 | 0.047111 | 0.895825 | 2.936526 | 0.053846 | 0.049250 | 0.176508 | 0.165340 | 0.044872 | 0.587607 | -10.417488 | 193.652557 | 0.412286 |
| 8 | 9 | 0.300026 | 0.042308 | 0.983664 | 2.286129 | 0.059126 | 0.044423 | 0.137414 | 0.125069 | 0.098291 | 0.685897 | -1.633599 | 128.612904 | 0.410549 |
| 9 | 10 | 0.400077 | 0.039162 | 0.533914 | 1.847935 | 0.032092 | 0.040642 | 0.111075 | 0.103955 | 0.053419 | 0.739316 | -46.608626 | 84.793459 | 0.360934 |
| 10 | 11 | 0.500000 | 0.036691 | 0.727056 | 1.623932 | 0.043702 | 0.037940 | 0.097611 | 0.090763 | 0.072650 | 0.811966 | -27.294399 | 62.393162 | 0.331917 |
| 11 | 12 | 0.600051 | 0.034383 | 0.597983 | 1.452867 | 0.035944 | 0.035517 | 0.087329 | 0.081551 | 0.059829 | 0.871795 | -40.201661 | 45.286705 | 0.289122 |
| 12 | 13 | 0.699974 | 0.031943 | 0.427680 | 1.306519 | 0.025707 | 0.033109 | 0.078532 | 0.074636 | 0.042735 | 0.914530 | -57.232000 | 30.651925 | 0.228277 |
| 13 | 14 | 0.800026 | 0.029395 | 0.277635 | 1.177847 | 0.016688 | 0.030752 | 0.070798 | 0.069148 | 0.027778 | 0.942308 | -72.236486 | 17.784680 | 0.151381 |
| 14 | 15 | 0.899949 | 0.025149 | 0.363528 | 1.087431 | 0.021851 | 0.027515 | 0.065363 | 0.064525 | 0.036325 | 0.978632 | -63.647200 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.001539 | 0.213565 | 1.000000 | 0.012837 | 0.020377 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.047804609796257544 RMSE: 0.21864265319524812 LogLoss: 0.18607161263951758 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311523 Residual deviance: 724.5628596182814 AIC: 754.5628596182814 AUC: 0.7586264069870626 AUCPR: 0.2851334797515788 Gini: 0.5172528139741253 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2307176830303629:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1756.0 | 74.0 | 0.0404 | (74.0/1830.0) |
| 1 | 1 | 64.0 | 53.0 | 0.547 | (64.0/117.0) |
| 2 | Total | 1820.0 | 127.0 | 0.0709 | (138.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.230718 | 0.434426 | 113.0 |
| 1 | max f2 | 0.230718 | 0.445378 | 113.0 |
| 2 | max f0point5 | 0.359460 | 0.445104 | 52.0 |
| 3 | max accuracy | 0.359460 | 0.942476 | 52.0 |
| 4 | max precision | 0.811058 | 1.000000 | 0.0 |
| 5 | max recall | 0.022047 | 1.000000 | 366.0 |
| 6 | max specificity | 0.811058 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.230718 | 0.397065 | 113.0 |
| 8 | max min_per_class_accuracy | 0.040484 | 0.671038 | 251.0 |
| 9 | max mean_per_class_accuracy | 0.056113 | 0.707952 | 188.0 |
| 10 | max tns | 0.811058 | 1830.000000 | 0.0 |
| 11 | max fns | 0.811058 | 116.000000 | 0.0 |
| 12 | max fps | 0.001829 | 1830.000000 | 399.0 |
| 13 | max tps | 0.022047 | 117.000000 | 366.0 |
| 14 | max tnr | 0.811058 | 1.000000 | 0.0 |
| 15 | max fnr | 0.811058 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001829 | 1.000000 | 399.0 |
| 17 | max tpr | 0.022047 | 1.000000 | 366.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.93 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.418098 | 6.656410 | 6.656410 | 0.400000 | 0.492730 | 0.400000 | 0.492730 | 0.068376 | 0.068376 | 565.641026 | 565.641026 | 0.061819 |
| 1 | 2 | 0.020031 | 0.378641 | 10.510121 | 8.533859 | 0.631579 | 0.399031 | 0.512821 | 0.447082 | 0.102564 | 0.170940 | 951.012146 | 753.385930 | 0.160558 |
| 2 | 3 | 0.030303 | 0.352229 | 9.152564 | 8.743590 | 0.550000 | 0.365946 | 0.525424 | 0.419578 | 0.094017 | 0.264957 | 815.256410 | 774.358974 | 0.249657 |
| 3 | 4 | 0.040062 | 0.336169 | 3.503374 | 7.467127 | 0.210526 | 0.345513 | 0.448718 | 0.401537 | 0.034188 | 0.299145 | 250.337382 | 646.712689 | 0.275648 |
| 4 | 5 | 0.050334 | 0.308529 | 6.656410 | 7.301675 | 0.400000 | 0.321062 | 0.438776 | 0.385113 | 0.068376 | 0.367521 | 565.641026 | 630.167452 | 0.337467 |
| 5 | 6 | 0.100154 | 0.067094 | 2.058684 | 4.693623 | 0.123711 | 0.154710 | 0.282051 | 0.270503 | 0.102564 | 0.470085 | 105.868358 | 369.362262 | 0.393583 |
| 6 | 7 | 0.149974 | 0.050944 | 1.200899 | 3.533368 | 0.072165 | 0.057453 | 0.212329 | 0.199729 | 0.059829 | 0.529915 | 20.089876 | 253.336846 | 0.404231 |
| 7 | 8 | 0.200308 | 0.046873 | 1.018838 | 2.901512 | 0.061224 | 0.048821 | 0.174359 | 0.161809 | 0.051282 | 0.581197 | 1.883830 | 190.151216 | 0.405240 |
| 8 | 9 | 0.299949 | 0.042392 | 0.686228 | 2.165613 | 0.041237 | 0.044266 | 0.130137 | 0.122762 | 0.068376 | 0.649573 | -31.377214 | 116.561293 | 0.371977 |
| 9 | 10 | 0.400103 | 0.039081 | 0.426693 | 1.730325 | 0.025641 | 0.040617 | 0.103979 | 0.102199 | 0.042735 | 0.692308 | -57.330703 | 73.032487 | 0.310887 |
| 10 | 11 | 0.500257 | 0.036482 | 0.682709 | 1.520587 | 0.041026 | 0.037680 | 0.091376 | 0.089282 | 0.068376 | 0.760684 | -31.729126 | 52.058653 | 0.277077 |
| 11 | 12 | 0.599897 | 0.034424 | 0.772006 | 1.396250 | 0.046392 | 0.035511 | 0.083904 | 0.080351 | 0.076923 | 0.837607 | -22.799366 | 39.625044 | 0.252907 |
| 12 | 13 | 0.700051 | 0.032167 | 0.682709 | 1.294166 | 0.041026 | 0.033268 | 0.077770 | 0.073615 | 0.068376 | 0.905983 | -31.729126 | 29.416634 | 0.219098 |
| 13 | 14 | 0.799692 | 0.029432 | 0.428892 | 1.186354 | 0.025773 | 0.030817 | 0.071291 | 0.068282 | 0.042735 | 0.948718 | -57.110759 | 18.635443 | 0.158554 |
| 14 | 15 | 0.899846 | 0.024899 | 0.256016 | 1.082806 | 0.015385 | 0.027359 | 0.065068 | 0.063728 | 0.025641 | 0.974359 | -74.398422 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.001773 | 0.256016 | 1.000000 | 0.015385 | 0.019868 | 0.060092 | 0.059335 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04853571422482637 RMSE: 0.22030822550423843 LogLoss: 0.1875385769453487 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.21015566259 Residual deviance: 2920.35072019297 AIC: 2950.35072019297 AUC: 0.7705311280229291 AUCPR: 0.27756568234211326 Gini: 0.5410622560458582 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.27480620464228317:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7032.0 | 286.0 | 0.0391 | (286.0/7318.0) |
| 1 | 1 | 275.0 | 193.0 | 0.5876 | (275.0/468.0) |
| 2 | Total | 7307.0 | 479.0 | 0.0721 | (561.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.274806 | 0.407603 | 142.0 |
| 1 | max f2 | 0.066232 | 0.439727 | 228.0 |
| 2 | max f0point5 | 0.307066 | 0.409357 | 121.0 |
| 3 | max accuracy | 0.415484 | 0.940277 | 45.0 |
| 4 | max precision | 0.858721 | 0.666667 | 1.0 |
| 5 | max recall | 0.018071 | 1.000000 | 384.0 |
| 6 | max specificity | 0.871186 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.274806 | 0.369278 | 142.0 |
| 8 | max min_per_class_accuracy | 0.042386 | 0.698688 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.060427 | 0.712322 | 237.0 |
| 10 | max tns | 0.871186 | 7317.000000 | 0.0 |
| 11 | max fns | 0.871186 | 467.000000 | 0.0 |
| 12 | max fps | 0.002059 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018071 | 468.000000 | 384.0 |
| 14 | max tnr | 0.871186 | 0.999863 | 0.0 |
| 15 | max fnr | 0.871186 | 0.997863 | 0.0 |
| 16 | max fpr | 0.002059 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018071 | 1.000000 | 384.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.03 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.409676 | 8.318376 | 8.318376 | 0.500000 | 0.498295 | 0.500000 | 0.498295 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.375484 | 7.038626 | 7.678501 | 0.423077 | 0.390921 | 0.461538 | 0.444608 | 0.070513 | 0.153846 | 603.862590 | 667.850099 | 0.142368 |
| 2 | 3 | 0.030054 | 0.350534 | 7.678501 | 7.678501 | 0.461538 | 0.361714 | 0.461538 | 0.416977 | 0.076923 | 0.230769 | 667.850099 | 667.850099 | 0.213551 |
| 3 | 4 | 0.040072 | 0.330352 | 5.119001 | 7.038626 | 0.307692 | 0.340700 | 0.423077 | 0.397907 | 0.051282 | 0.282051 | 411.900066 | 603.862590 | 0.257454 |
| 4 | 5 | 0.050090 | 0.307945 | 7.038626 | 7.038626 | 0.423077 | 0.320256 | 0.423077 | 0.382377 | 0.070513 | 0.352564 | 603.862590 | 603.862590 | 0.321818 |
| 5 | 6 | 0.100051 | 0.065055 | 2.865456 | 4.954720 | 0.172237 | 0.159881 | 0.297818 | 0.271272 | 0.143162 | 0.495726 | 186.545602 | 395.471951 | 0.420979 |
| 6 | 7 | 0.150013 | 0.058172 | 0.983664 | 3.632168 | 0.059126 | 0.060375 | 0.218322 | 0.201033 | 0.049145 | 0.544872 | -1.633599 | 263.216763 | 0.420111 |
| 7 | 8 | 0.200103 | 0.049435 | 0.682533 | 2.893812 | 0.041026 | 0.052936 | 0.173941 | 0.163961 | 0.034188 | 0.579060 | -31.746658 | 189.381247 | 0.403192 |
| 8 | 9 | 0.300026 | 0.043208 | 0.962280 | 2.250520 | 0.057841 | 0.045898 | 0.135274 | 0.124641 | 0.096154 | 0.675214 | -3.771999 | 125.051955 | 0.399182 |
| 9 | 10 | 0.400077 | 0.039867 | 0.640696 | 1.847935 | 0.038511 | 0.041437 | 0.111075 | 0.103833 | 0.064103 | 0.739316 | -35.930351 | 84.793459 | 0.360934 |
| 10 | 11 | 0.500000 | 0.037192 | 0.534600 | 1.585470 | 0.032134 | 0.038440 | 0.095299 | 0.090764 | 0.053419 | 0.792735 | -46.540000 | 58.547009 | 0.311456 |
| 11 | 12 | 0.600051 | 0.034710 | 0.640696 | 1.427940 | 0.038511 | 0.035953 | 0.085830 | 0.081625 | 0.064103 | 0.856838 | -35.930351 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.032310 | 0.491832 | 1.294309 | 0.029563 | 0.033551 | 0.077798 | 0.074763 | 0.049145 | 0.905983 | -50.816800 | 29.430879 | 0.219183 |
| 13 | 14 | 0.800026 | 0.029695 | 0.341705 | 1.175176 | 0.020539 | 0.031061 | 0.070637 | 0.069297 | 0.034188 | 0.940171 | -65.829521 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.025425 | 0.342144 | 1.082683 | 0.020566 | 0.027826 | 0.065078 | 0.064693 | 0.034188 | 0.974359 | -65.785600 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.001591 | 0.256279 | 1.000000 | 0.015404 | 0.020688 | 0.060108 | 0.060290 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93065095 | 0.019844683 | 0.9307692 | 0.9423077 | 0.90384614 | 0.86153847 | 0.9346154 | 0.9423077 | 0.9461538 | 0.9307692 | 0.93846154 | 0.9076923 | 0.9461538 | 0.9423077 | 0.9115385 | 0.9153846 | 0.9423077 | 0.9461538 | 0.9459459 | 0.93822396 | 0.9150579 | 0.93822396 | 0.94208497 | 0.96138996 | 0.93822396 | 0.8996139 | 0.94208497 | 0.93050194 | 0.9459459 | 0.9189189 | 0.94208497 | 0.9189189 |
| 1 | auc | 0.777867 | 0.070473514 | 0.7337278 | 0.75642794 | 0.7126604 | 0.714375 | 0.7872186 | 0.79604167 | 0.8110236 | 0.7155647 | 0.74564546 | 0.9175607 | 0.78939724 | 0.77296793 | 0.6584 | 0.75816995 | 0.8173617 | 0.7548668 | 0.7754847 | 0.74385965 | 0.8104424 | 0.58461934 | 0.8333333 | 0.8441767 | 0.9079235 | 0.81311476 | 0.70479083 | 0.8771107 | 0.8267544 | 0.74225944 | 0.772807 | 0.8579235 |
| 2 | err | 0.06934907 | 0.019844683 | 0.06923077 | 0.057692308 | 0.09615385 | 0.13846155 | 0.06538462 | 0.057692308 | 0.053846154 | 0.06923077 | 0.06153846 | 0.092307694 | 0.053846154 | 0.057692308 | 0.08846154 | 0.08461539 | 0.057692308 | 0.053846154 | 0.054054055 | 0.06177606 | 0.08494209 | 0.06177606 | 0.057915058 | 0.038610037 | 0.06177606 | 0.1003861 | 0.057915058 | 0.06949807 | 0.054054055 | 0.08108108 | 0.057915058 | 0.08108108 |
| 3 | err_count | 18.0 | 5.159524 | 18.0 | 15.0 | 25.0 | 36.0 | 17.0 | 15.0 | 14.0 | 18.0 | 16.0 | 24.0 | 14.0 | 15.0 | 23.0 | 22.0 | 15.0 | 14.0 | 14.0 | 16.0 | 22.0 | 16.0 | 15.0 | 10.0 | 16.0 | 26.0 | 15.0 | 18.0 | 14.0 | 21.0 | 15.0 | 21.0 |
| 4 | f0point5 | 0.44902354 | 0.12538917 | 0.37037036 | 0.5769231 | 0.3539823 | 0.28846154 | 0.46153846 | 0.625 | 0.2857143 | 0.5 | 0.41666666 | 0.24390244 | 0.58441556 | 0.4347826 | 0.21276596 | 0.42735043 | 0.72164947 | 0.5555556 | 0.46153846 | 0.5555556 | 0.36458334 | 0.3125 | 0.5263158 | 0.5 | 0.5140187 | 0.34351146 | 0.35714287 | 0.37037036 | 0.63492066 | 0.46875 | 0.5970149 | 0.4054054 |
| 5 | f1 | 0.4432193 | 0.099002786 | 0.4 | 0.54545456 | 0.3902439 | 0.33333334 | 0.41379312 | 0.516129 | 0.36363637 | 0.5 | 0.33333334 | 0.33333334 | 0.5625 | 0.44444445 | 0.2580645 | 0.47619048 | 0.6511628 | 0.53333336 | 0.46153846 | 0.46666667 | 0.3888889 | 0.2 | 0.516129 | 0.5 | 0.57894737 | 0.4090909 | 0.3478261 | 0.4 | 0.53333336 | 0.46153846 | 0.516129 | 0.46153846 |
| 6 | f2 | 0.45496523 | 0.09798086 | 0.4347826 | 0.51724136 | 0.4347826 | 0.39473686 | 0.375 | 0.43956044 | 0.5 | 0.5 | 0.2777778 | 0.5263158 | 0.5421687 | 0.45454547 | 0.32786885 | 0.53763443 | 0.59322035 | 0.51282054 | 0.46153846 | 0.40229884 | 0.41666666 | 0.14705883 | 0.5063291 | 0.5 | 0.6626506 | 0.505618 | 0.33898306 | 0.4347826 | 0.4597701 | 0.45454547 | 0.45454547 | 0.53571427 |
| 7 | lift_top_group | 7.8529162 | 4.193273 | 0.0 | 9.62963 | 15.294118 | 0.0 | 10.196078 | 13.0 | 0.0 | 9.62963 | 5.4166665 | 12.380953 | 5.098039 | 13.333333 | 8.666667 | 0.0 | 10.4 | 10.833333 | 6.6410255 | 4.5438595 | 5.3958335 | 5.3958335 | 10.791667 | 8.633333 | 5.7555556 | 11.511111 | 7.1944447 | 13.282051 | 9.087719 | 8.633333 | 9.087719 | 5.7555556 |
| 8 | logloss | 0.18596675 | 0.035637114 | 0.16905476 | 0.19634143 | 0.2125495 | 0.25089756 | 0.20687021 | 0.20951349 | 0.10165646 | 0.20749132 | 0.20690708 | 0.11338346 | 0.18433242 | 0.16115186 | 0.15379101 | 0.19413336 | 0.2333488 | 0.17788923 | 0.15895338 | 0.23145203 | 0.19755971 | 0.2283506 | 0.17391366 | 0.12577467 | 0.15314393 | 0.18074198 | 0.1762455 | 0.15983959 | 0.2025969 | 0.2295321 | 0.20829573 | 0.17329058 |
| 9 | max_per_class_error | 0.52130556 | 0.13726413 | 0.53846157 | 0.5 | 0.5294118 | 0.55 | 0.64705884 | 0.6 | 0.33333334 | 0.5 | 0.75 | 0.14285715 | 0.47058824 | 0.53846157 | 0.6 | 0.4117647 | 0.44 | 0.5 | 0.53846157 | 0.6315789 | 0.5625 | 0.875 | 0.5 | 0.5 | 0.26666668 | 0.4 | 0.6666667 | 0.53846157 | 0.57894737 | 0.55 | 0.57894737 | 0.4 |
| 10 | mcc | 0.42249408 | 0.095802225 | 0.3676485 | 0.5173658 | 0.34565836 | 0.2733341 | 0.38673693 | 0.51297516 | 0.38701215 | 0.46280992 | 0.32508332 | 0.3939722 | 0.5351183 | 0.41437876 | 0.23431468 | 0.44149697 | 0.6305838 | 0.5061643 | 0.43308318 | 0.4547783 | 0.34632412 | 0.22797784 | 0.4856482 | 0.47991967 | 0.5617898 | 0.38373807 | 0.31791216 | 0.3675012 | 0.528212 | 0.41788897 | 0.50157905 | 0.43381697 |
| 11 | mean_per_class_accuracy | 0.71952873 | 0.06294536 | 0.708502 | 0.7376033 | 0.7023723 | 0.67291665 | 0.6641249 | 0.69375 | 0.8097113 | 0.73140496 | 0.6168033 | 0.8831169 | 0.7523602 | 0.7145749 | 0.666 | 0.76325345 | 0.7714894 | 0.73770493 | 0.7165416 | 0.6758772 | 0.69200104 | 0.5583848 | 0.7355967 | 0.73995984 | 0.8420765 | 0.7590164 | 0.65249664 | 0.7084115 | 0.7042763 | 0.7040795 | 0.70219296 | 0.7692623 |
| 12 | mean_per_class_error | 0.28047127 | 0.06294536 | 0.29149798 | 0.2623967 | 0.2976277 | 0.32708332 | 0.3358751 | 0.30625 | 0.19028871 | 0.26859504 | 0.3831967 | 0.116883114 | 0.24763979 | 0.2854251 | 0.334 | 0.23674655 | 0.22851063 | 0.26229507 | 0.2834584 | 0.32412282 | 0.30799899 | 0.44161522 | 0.26440328 | 0.26004016 | 0.1579235 | 0.2409836 | 0.34750336 | 0.2915885 | 0.29572368 | 0.2959205 | 0.297807 | 0.2307377 |
| 13 | mse | 0.048152007 | 0.0106398305 | 0.04299763 | 0.051001545 | 0.055037305 | 0.067483574 | 0.0552306 | 0.055276193 | 0.022375565 | 0.054551378 | 0.052791175 | 0.026984215 | 0.048269894 | 0.040533494 | 0.035657976 | 0.05221419 | 0.064392805 | 0.045153223 | 0.039496087 | 0.06163197 | 0.05272008 | 0.057475522 | 0.045075823 | 0.03048617 | 0.04211362 | 0.047566377 | 0.042555355 | 0.040959284 | 0.05375936 | 0.060842678 | 0.054601707 | 0.0453254 |
| 14 | null_deviance | 118.04034 | 22.137926 | 103.75808 | 131.24835 | 125.73113 | 142.31174 | 125.73113 | 142.31174 | 65.66958 | 131.24835 | 120.223526 | 71.082695 | 125.73113 | 103.75808 | 87.37807 | 125.73113 | 170.14055 | 120.223526 | 103.63261 | 136.65318 | 120.09978 | 120.09978 | 120.09978 | 87.25086 | 114.60118 | 114.60118 | 98.16257 | 103.63261 | 136.65318 | 142.19029 | 136.65318 | 114.60118 |
| 15 | pr_auc | 0.31108263 | 0.10248512 | 0.19936539 | 0.4266331 | 0.35184535 | 0.20736526 | 0.33151186 | 0.47166553 | 0.14636007 | 0.28409392 | 0.20778188 | 0.269083 | 0.33805987 | 0.3218172 | 0.20057197 | 0.25377166 | 0.5516612 | 0.34904793 | 0.25017473 | 0.26239654 | 0.24869502 | 0.108561136 | 0.4235789 | 0.29937318 | 0.40585774 | 0.32424527 | 0.20783252 | 0.34056285 | 0.47298652 | 0.33873212 | 0.4238791 | 0.31496775 |
| 16 | precision | 0.46441486 | 0.15493125 | 0.3529412 | 0.6 | 0.33333334 | 0.2647059 | 0.5 | 0.72727275 | 0.25 | 0.5 | 0.5 | 0.20689656 | 0.6 | 0.42857143 | 0.1904762 | 0.4 | 0.7777778 | 0.5714286 | 0.46153846 | 0.6363636 | 0.35 | 0.5 | 0.53333336 | 0.5 | 0.47826087 | 0.31034482 | 0.36363637 | 0.3529412 | 0.72727275 | 0.47368422 | 0.6666667 | 0.375 |
| 17 | r2 | 0.13407366 | 0.0762124 | 0.094786756 | 0.20851599 | 0.09936536 | 0.049606353 | 0.09620227 | 0.22152697 | 0.0074880654 | 0.15342675 | 0.085890554 | -0.03000162 | 0.21010779 | 0.14666326 | 0.0358083 | 0.14556298 | 0.2590717 | 0.21814604 | 0.17153315 | 0.09334776 | 0.09040235 | 0.008355042 | 0.22229135 | 0.1786977 | 0.22813556 | 0.12819666 | 0.03689079 | 0.14084122 | 0.20915976 | 0.14615323 | 0.19676816 | 0.16926965 |
| 18 | recall | 0.47869444 | 0.13726413 | 0.46153846 | 0.5 | 0.47058824 | 0.45 | 0.3529412 | 0.4 | 0.6666667 | 0.5 | 0.25 | 0.85714287 | 0.5294118 | 0.46153846 | 0.4 | 0.5882353 | 0.56 | 0.5 | 0.46153846 | 0.36842105 | 0.4375 | 0.125 | 0.5 | 0.5 | 0.73333335 | 0.6 | 0.33333334 | 0.46153846 | 0.42105263 | 0.45 | 0.42105263 | 0.6 |
| 19 | residual_deviance | 96.529396 | 18.508106 | 87.90847 | 102.09754 | 110.525734 | 130.46672 | 107.57251 | 108.947014 | 52.86136 | 107.895485 | 107.591675 | 58.959396 | 95.85286 | 83.798965 | 79.97133 | 100.94934 | 121.34138 | 92.502396 | 82.33785 | 119.89215 | 102.33593 | 118.285614 | 90.08728 | 65.151276 | 79.32855 | 93.624344 | 91.295166 | 82.796906 | 104.9452 | 118.89763 | 107.89719 | 89.76453 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:14:03 | 0.000 sec | 2 | .84E1 | 15.0 | 0.452081 | 0.451991 | 0.452483 | 0.015455 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:14:03 | 0.003 sec | 4 | .52E1 | 15.0 | 0.450624 | 0.45053 | 0.451091 | 0.015393 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:14:03 | 0.009 sec | 6 | .32E1 | 15.0 | 0.448336 | 0.448234 | 0.448904 | 0.015297 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:14:03 | 0.026 sec | 8 | .2E1 | 15.0 | 0.444781 | 0.444667 | 0.445502 | 0.015148 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:14:03 | 0.030 sec | 10 | .12E1 | 15.0 | 0.439431 | 0.439292 | 0.440369 | 0.014928 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:14:03 | 0.033 sec | 12 | .78E0 | 15.0 | 0.431739 | 0.431552 | 0.432958 | 0.01462 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:14:03 | 0.035 sec | 14 | .48E0 | 15.0 | 0.421524 | 0.421253 | 0.423049 | 0.014228 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:14:03 | 0.038 sec | 16 | .3E0 | 15.0 | 0.409625 | 0.409223 | 0.411385 | 0.013807 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:14:03 | 0.040 sec | 18 | .19E0 | 15.0 | 0.398004 | 0.397436 | 0.399865 | 0.013454 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:14:03 | 0.043 sec | 20 | .12E0 | 15.0 | 0.388602 | 0.387878 | 0.390488 | 0.013247 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:14:03 | 0.046 sec | 22 | .72E-1 | 15.0 | 0.382074 | 0.381246 | 0.384004 | 0.013181 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:14:03 | 0.048 sec | 24 | .44E-1 | 15.0 | 0.377961 | 0.377076 | 0.380011 | 0.013209 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:14:03 | 0.051 sec | 26 | .28E-1 | 15.0 | 0.375495 | 0.37457 | 0.377732 | 0.013279 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:14:03 | 0.053 sec | 28 | .17E-1 | 15.0 | 0.374031 | 0.373062 | 0.375868 | 0.013205 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:14:03 | 0.275 sec | 29 | None | NaN | 29.0 | 0.219812 | 0.186583 | 0.144751 | 0.776182 | 0.286432 | 8.744959 | 0.071025 | 0.218643 | 0.186072 | 0.153621 | 0.758626 | 0.285133 | 6.65641 | 0.070878 | ||||||
| 15 | 2021-07-15 20:14:03 | 0.056 sec | 30 | .11E-1 | 15.0 | 0.373165 | 0.372143 | 0.375077 | 0.01324 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:14:03 | 0.060 sec | 32 | .66E-2 | 15.0 | 0.372658 | 0.371581 | 0.375553 | 0.01364 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:14:03 | 0.063 sec | 34 | .41E-2 | 15.0 | 0.372371 | 0.371243 | 0.376738 | 0.01353 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:14:03 | 0.064 sec | 35 | .26E-2 | 15.0 | 0.372217 | 0.371043 | 0.376541 | 0.013533 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.534898 | 1.000000 | 0.269557 |
| 1 | Average_Transaction_Frequency | 0.220452 | 0.412139 | 0.111095 |
| 2 | Minimum_Transaction_Amount | 0.210082 | 0.392752 | 0.105869 |
| 3 | Channel_ID | 0.185599 | 0.346979 | 0.093531 |
| 4 | Merchant_ID | 0.170511 | 0.318774 | 0.085928 |
| 5 | Card_Type.1 | 0.156135 | 0.291896 | 0.078683 |
| 6 | Card_Type.0 | 0.154669 | 0.289156 | 0.077944 |
| 7 | Transaction_Amount | 0.132861 | 0.248387 | 0.066954 |
| 8 | Transaction_Date | 0.065369 | 0.122209 | 0.032942 |
| 9 | Average_Transaction_Amount | 0.048109 | 0.089940 | 0.024244 |
| 10 | Month | 0.046644 | 0.087201 | 0.023506 |
| 11 | Day | 0.038963 | 0.072842 | 0.019635 |
| 12 | City_ID | 0.018279 | 0.034174 | 0.009212 |
| 13 | Maximum_Transaction_Amount | 0.001787 | 0.003341 | 0.000900 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201406 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.003977 ) | nlambda = 30, lambda.max = 8.1255, lambda.min = 0.003977, lambda.1... | 14 | 14 | 34 | automl_training_py_502_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04854498512135318 RMSE: 0.22032926524035154 LogLoss: 0.1880442870569787 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938407 Residual deviance: 2928.2256380512717 AIC: 2958.2256380512717 AUC: 0.767535061655723 AUCPR: 0.2826277044671029 Gini: 0.5350701233114461 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.29696331955141025:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7073.0 | 245.0 | 0.0335 | (245.0/7318.0) |
| 1 | 1 | 290.0 | 178.0 | 0.6197 | (290.0/468.0) |
| 2 | Total | 7363.0 | 423.0 | 0.0687 | (535.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.296963 | 0.399551 | 131.0 |
| 1 | max f2 | 0.073431 | 0.432288 | 222.0 |
| 2 | max f0point5 | 0.310667 | 0.413405 | 122.0 |
| 3 | max accuracy | 0.441146 | 0.940791 | 47.0 |
| 4 | max precision | 0.869098 | 1.000000 | 0.0 |
| 5 | max recall | 0.016588 | 1.000000 | 380.0 |
| 6 | max specificity | 0.869098 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.296963 | 0.363730 | 131.0 |
| 8 | max min_per_class_accuracy | 0.042300 | 0.685023 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.061590 | 0.707925 | 237.0 |
| 10 | max tns | 0.869098 | 7318.000000 | 0.0 |
| 11 | max fns | 0.869098 | 467.000000 | 0.0 |
| 12 | max fps | 0.000778 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016588 | 468.000000 | 380.0 |
| 14 | max tnr | 0.869098 | 1.000000 | 0.0 |
| 15 | max fnr | 0.869098 | 0.997863 | 0.0 |
| 16 | max fpr | 0.000778 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016588 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.432770 | 8.744959 | 8.744959 | 0.525641 | 0.511032 | 0.525641 | 0.511032 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.396849 | 7.678501 | 8.211730 | 0.461538 | 0.412266 | 0.493590 | 0.461649 | 0.076923 | 0.164530 | 667.850099 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.365710 | 6.185459 | 7.536307 | 0.371795 | 0.379889 | 0.452991 | 0.434396 | 0.061966 | 0.226496 | 518.545913 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.343750 | 6.612043 | 7.305241 | 0.397436 | 0.354165 | 0.439103 | 0.414338 | 0.066239 | 0.292735 | 561.204252 | 630.524052 | 0.268821 |
| 4 | 5 | 0.050090 | 0.312969 | 6.398751 | 7.123943 | 0.384615 | 0.328933 | 0.428205 | 0.397257 | 0.064103 | 0.356838 | 539.875082 | 612.394258 | 0.326365 |
| 5 | 6 | 0.100051 | 0.070181 | 2.523312 | 4.826580 | 0.151671 | 0.160423 | 0.290116 | 0.278992 | 0.126068 | 0.482906 | 152.331202 | 382.658021 | 0.407339 |
| 6 | 7 | 0.150013 | 0.054283 | 0.983664 | 3.546705 | 0.059126 | 0.060505 | 0.213185 | 0.206226 | 0.049145 | 0.532051 | -1.633599 | 254.670486 | 0.406471 |
| 7 | 8 | 0.200103 | 0.049075 | 0.810508 | 2.861778 | 0.048718 | 0.051399 | 0.172015 | 0.167469 | 0.040598 | 0.572650 | -18.949156 | 186.177765 | 0.396372 |
| 8 | 9 | 0.300026 | 0.043666 | 0.919512 | 2.214910 | 0.055270 | 0.046021 | 0.133134 | 0.127021 | 0.091880 | 0.664530 | -8.048799 | 121.491007 | 0.387815 |
| 9 | 10 | 0.400077 | 0.040030 | 0.512557 | 1.789185 | 0.030809 | 0.041701 | 0.107544 | 0.105684 | 0.051282 | 0.715812 | -48.744281 | 78.918522 | 0.335927 |
| 10 | 11 | 0.500000 | 0.037114 | 0.812592 | 1.594017 | 0.048843 | 0.038544 | 0.095813 | 0.092267 | 0.081197 | 0.797009 | -18.740799 | 59.401709 | 0.316003 |
| 11 | 12 | 0.600051 | 0.034335 | 0.704766 | 1.445745 | 0.042362 | 0.035751 | 0.086901 | 0.082843 | 0.070513 | 0.867521 | -29.523386 | 44.574516 | 0.284575 |
| 12 | 13 | 0.699974 | 0.031296 | 0.406296 | 1.297361 | 0.024422 | 0.032877 | 0.077982 | 0.075710 | 0.040598 | 0.908120 | -59.370400 | 29.736141 | 0.221457 |
| 13 | 14 | 0.800026 | 0.027507 | 0.363061 | 1.180518 | 0.021823 | 0.029574 | 0.070958 | 0.069941 | 0.036325 | 0.944444 | -63.693866 | 18.051765 | 0.153655 |
| 14 | 15 | 0.899949 | 0.021698 | 0.342144 | 1.087431 | 0.020566 | 0.024802 | 0.065363 | 0.064929 | 0.034188 | 0.978632 | -65.785600 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000538 | 0.213565 | 1.000000 | 0.012837 | 0.016746 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04615084630051092 RMSE: 0.2148274803196996 LogLoss: 0.1777510424592538 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311453 Residual deviance: 692.1625593363344 AIC: 722.1625593363344 AUC: 0.788491896688618 AUCPR: 0.30649907956373157 Gini: 0.5769837933772359 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.23672679334522992:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1759.0 | 71.0 | 0.0388 | (71.0/1830.0) |
| 1 | 1 | 58.0 | 59.0 | 0.4957 | (58.0/117.0) |
| 2 | Total | 1817.0 | 130.0 | 0.0663 | (129.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.236727 | 0.477733 | 108.0 |
| 1 | max f2 | 0.136961 | 0.508065 | 126.0 |
| 2 | max f0point5 | 0.317551 | 0.479042 | 76.0 |
| 3 | max accuracy | 0.792704 | 0.940421 | 2.0 |
| 4 | max precision | 0.792704 | 0.666667 | 2.0 |
| 5 | max recall | 0.015970 | 1.000000 | 376.0 |
| 6 | max specificity | 0.873118 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.236727 | 0.443166 | 108.0 |
| 8 | max min_per_class_accuracy | 0.043490 | 0.700855 | 241.0 |
| 9 | max mean_per_class_accuracy | 0.055783 | 0.745222 | 191.0 |
| 10 | max tns | 0.873118 | 1829.000000 | 0.0 |
| 11 | max fns | 0.873118 | 117.000000 | 0.0 |
| 12 | max fps | 0.000766 | 1830.000000 | 399.0 |
| 13 | max tps | 0.015970 | 117.000000 | 376.0 |
| 14 | max tnr | 0.873118 | 0.999454 | 0.0 |
| 15 | max fnr | 0.873118 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000766 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015970 | 1.000000 | 376.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.17 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.450229 | 8.320513 | 8.320513 | 0.500000 | 0.556150 | 0.500000 | 0.556150 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.400636 | 7.006748 | 7.680473 | 0.421053 | 0.422135 | 0.461538 | 0.490861 | 0.068376 | 0.153846 | 600.674764 | 668.047337 | 0.142371 |
| 2 | 3 | 0.030303 | 0.367519 | 8.320513 | 7.897436 | 0.500000 | 0.382265 | 0.474576 | 0.454049 | 0.085470 | 0.239316 | 732.051282 | 689.743590 | 0.222376 |
| 3 | 4 | 0.040062 | 0.340861 | 9.634278 | 8.320513 | 0.578947 | 0.356087 | 0.500000 | 0.430186 | 0.094017 | 0.333333 | 863.427800 | 732.051282 | 0.312022 |
| 4 | 5 | 0.050334 | 0.310892 | 7.488462 | 8.150706 | 0.450000 | 0.328676 | 0.489796 | 0.409470 | 0.076923 | 0.410256 | 648.846154 | 715.070644 | 0.382934 |
| 5 | 6 | 0.100154 | 0.074312 | 2.573354 | 5.376331 | 0.154639 | 0.174731 | 0.323077 | 0.292702 | 0.128205 | 0.538462 | 157.335448 | 437.633136 | 0.466330 |
| 6 | 7 | 0.149974 | 0.054960 | 1.372456 | 4.046277 | 0.082474 | 0.062148 | 0.243151 | 0.216114 | 0.068376 | 0.606838 | 37.245572 | 304.627678 | 0.486073 |
| 7 | 8 | 0.200308 | 0.050264 | 0.509419 | 3.157528 | 0.030612 | 0.052234 | 0.189744 | 0.174934 | 0.025641 | 0.632479 | -49.058085 | 215.752794 | 0.459801 |
| 8 | 9 | 0.299949 | 0.044157 | 0.600449 | 2.308087 | 0.036082 | 0.046859 | 0.138699 | 0.132389 | 0.059829 | 0.692308 | -39.955062 | 130.808746 | 0.417444 |
| 9 | 10 | 0.400103 | 0.040050 | 0.682709 | 1.901221 | 0.041026 | 0.041950 | 0.114249 | 0.109750 | 0.068376 | 0.760684 | -31.729126 | 90.122116 | 0.383635 |
| 10 | 11 | 0.500257 | 0.037284 | 0.597370 | 1.640183 | 0.035897 | 0.038614 | 0.098563 | 0.095508 | 0.059829 | 0.820513 | -40.262985 | 64.018323 | 0.340731 |
| 11 | 12 | 0.599897 | 0.034248 | 0.428892 | 1.438993 | 0.025773 | 0.035779 | 0.086473 | 0.085587 | 0.042735 | 0.863248 | -57.110759 | 43.899280 | 0.280188 |
| 12 | 13 | 0.700051 | 0.031133 | 0.341354 | 1.281957 | 0.020513 | 0.032701 | 0.077036 | 0.078021 | 0.034188 | 0.897436 | -65.864563 | 28.195722 | 0.210004 |
| 13 | 14 | 0.799692 | 0.027049 | 0.343114 | 1.164979 | 0.020619 | 0.029420 | 0.070006 | 0.071965 | 0.034188 | 0.931624 | -65.688607 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.021224 | 0.512032 | 1.092305 | 0.030769 | 0.024336 | 0.065639 | 0.066664 | 0.051282 | 0.982906 | -48.796844 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.000750 | 0.170677 | 1.000000 | 0.010256 | 0.016655 | 0.060092 | 0.061656 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04891401549006417 RMSE: 0.22116513172302765 LogLoss: 0.19000372159299989 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.79603373249 Residual deviance: 2958.737952646195 AIC: 2988.737952646195 AUC: 0.7560169515280202 AUCPR: 0.2703017803189565 Gini: 0.5120339030560404 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.25475047226958236:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7048.0 | 270.0 | 0.0369 | (270.0/7318.0) |
| 1 | 1 | 287.0 | 181.0 | 0.6132 | (287.0/468.0) |
| 2 | Total | 7335.0 | 451.0 | 0.0715 | (557.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.254750 | 0.393906 | 143.0 |
| 1 | max f2 | 0.075758 | 0.419608 | 211.0 |
| 2 | max f0point5 | 0.310162 | 0.406504 | 116.0 |
| 3 | max accuracy | 0.460547 | 0.940406 | 32.0 |
| 4 | max precision | 0.570713 | 0.600000 | 12.0 |
| 5 | max recall | 0.015966 | 1.000000 | 383.0 |
| 6 | max specificity | 0.884742 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.254750 | 0.355976 | 143.0 |
| 8 | max min_per_class_accuracy | 0.044305 | 0.683761 | 273.0 |
| 9 | max mean_per_class_accuracy | 0.059110 | 0.700130 | 233.0 |
| 10 | max tns | 0.884742 | 7317.000000 | 0.0 |
| 11 | max fns | 0.884742 | 468.000000 | 0.0 |
| 12 | max fps | 0.000719 | 7318.000000 | 399.0 |
| 13 | max tps | 0.015966 | 468.000000 | 383.0 |
| 14 | max tnr | 0.884742 | 0.999863 | 0.0 |
| 15 | max fnr | 0.884742 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000719 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015966 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.02 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.428863 | 8.318376 | 8.318376 | 0.500000 | 0.508976 | 0.500000 | 0.508976 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.390317 | 7.465209 | 7.891793 | 0.448718 | 0.408180 | 0.474359 | 0.458578 | 0.074786 | 0.158120 | 646.520929 | 689.179268 | 0.146914 |
| 2 | 3 | 0.030054 | 0.358916 | 6.398751 | 7.394112 | 0.384615 | 0.373582 | 0.444444 | 0.430246 | 0.064103 | 0.222222 | 539.875082 | 639.411206 | 0.204458 |
| 3 | 4 | 0.040072 | 0.335857 | 5.972167 | 7.038626 | 0.358974 | 0.346520 | 0.423077 | 0.409315 | 0.059829 | 0.282051 | 497.216743 | 603.862590 | 0.257454 |
| 4 | 5 | 0.050090 | 0.301836 | 6.612043 | 6.953309 | 0.397436 | 0.318730 | 0.417949 | 0.391198 | 0.066239 | 0.348291 | 561.204252 | 595.330923 | 0.317271 |
| 5 | 6 | 0.100051 | 0.066164 | 2.437776 | 4.698441 | 0.146530 | 0.138430 | 0.282413 | 0.264976 | 0.121795 | 0.470085 | 143.777602 | 369.844091 | 0.393698 |
| 6 | 7 | 0.150013 | 0.060300 | 0.769824 | 3.390023 | 0.046272 | 0.061527 | 0.203767 | 0.197218 | 0.038462 | 0.508547 | -23.017599 | 239.002312 | 0.381463 |
| 7 | 8 | 0.200103 | 0.055581 | 1.279750 | 2.861778 | 0.076923 | 0.058669 | 0.172015 | 0.162536 | 0.064103 | 0.572650 | 27.975016 | 186.177765 | 0.396372 |
| 8 | 9 | 0.300026 | 0.046004 | 0.769824 | 2.165057 | 0.046272 | 0.049785 | 0.130137 | 0.124985 | 0.076923 | 0.649573 | -23.017599 | 116.505678 | 0.371901 |
| 9 | 10 | 0.400077 | 0.041600 | 0.662053 | 1.789185 | 0.039795 | 0.043686 | 0.107544 | 0.104653 | 0.066239 | 0.715812 | -33.794696 | 78.918522 | 0.335927 |
| 10 | 11 | 0.500000 | 0.038268 | 0.555984 | 1.542735 | 0.033419 | 0.039867 | 0.092731 | 0.091706 | 0.055556 | 0.771368 | -44.401600 | 54.273504 | 0.288722 |
| 11 | 12 | 0.600051 | 0.035283 | 0.640696 | 1.392331 | 0.038511 | 0.036748 | 0.083690 | 0.082543 | 0.064103 | 0.835470 | -35.930351 | 39.233093 | 0.250474 |
| 12 | 13 | 0.699974 | 0.032150 | 0.748440 | 1.300414 | 0.044987 | 0.033721 | 0.078165 | 0.075573 | 0.074786 | 0.910256 | -25.155999 | 30.041402 | 0.223730 |
| 13 | 14 | 0.800026 | 0.028183 | 0.256279 | 1.169834 | 0.015404 | 0.030316 | 0.070316 | 0.069913 | 0.025641 | 0.935897 | -74.372140 | 16.983423 | 0.144561 |
| 14 | 15 | 0.899949 | 0.022150 | 0.320760 | 1.075560 | 0.019280 | 0.025518 | 0.064650 | 0.064984 | 0.032051 | 0.967949 | -67.924000 | 7.555997 | 0.072349 |
| 15 | 16 | 1.000000 | 0.000496 | 0.320348 | 1.000000 | 0.019255 | 0.017194 | 0.060108 | 0.060203 | 0.032051 | 1.000000 | -67.965176 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93282795 | 0.025574708 | 0.9307692 | 0.91923076 | 0.9115385 | 0.8923077 | 0.97307694 | 0.9115385 | 0.9153846 | 0.95 | 0.9576923 | 0.89615387 | 0.9423077 | 0.95 | 0.9346154 | 0.9269231 | 0.9807692 | 0.9346154 | 0.95366794 | 0.9034749 | 0.93050194 | 0.9266409 | 0.93436295 | 0.8841699 | 0.9189189 | 0.94208497 | 0.93436295 | 0.8880309 | 0.972973 | 0.95752895 | 0.9498069 | 0.96138996 |
| 1 | auc | 0.76530796 | 0.07694741 | 0.8308333 | 0.727459 | 0.74492246 | 0.8156338 | 0.8148959 | 0.7504762 | 0.6621533 | 0.7743902 | 0.6796 | 0.7571522 | 0.8106996 | 0.6867316 | 0.70696723 | 0.6821209 | 0.83085316 | 0.74615777 | 0.664305 | 0.8243313 | 0.8460251 | 0.72157437 | 0.77486336 | 0.6969697 | 0.79483634 | 0.7239067 | 0.7458159 | 0.6226971 | 0.97300947 | 0.8115836 | 0.8377111 | 0.9005628 |
| 2 | err | 0.067172065 | 0.025574708 | 0.06923077 | 0.08076923 | 0.08846154 | 0.10769231 | 0.026923077 | 0.08846154 | 0.08461539 | 0.05 | 0.042307694 | 0.103846155 | 0.057692308 | 0.05 | 0.06538462 | 0.073076926 | 0.01923077 | 0.06538462 | 0.046332046 | 0.096525095 | 0.06949807 | 0.07335907 | 0.06563707 | 0.115830116 | 0.08108108 | 0.057915058 | 0.06563707 | 0.11196911 | 0.027027028 | 0.042471044 | 0.05019305 | 0.038610037 |
| 3 | err_count | 17.433332 | 6.636801 | 18.0 | 21.0 | 23.0 | 28.0 | 7.0 | 23.0 | 22.0 | 13.0 | 11.0 | 27.0 | 15.0 | 13.0 | 17.0 | 19.0 | 5.0 | 17.0 | 12.0 | 25.0 | 18.0 | 19.0 | 17.0 | 30.0 | 21.0 | 15.0 | 17.0 | 29.0 | 7.0 | 11.0 | 13.0 | 10.0 |
| 4 | f0point5 | 0.46380767 | 0.12733662 | 0.5645161 | 0.35714287 | 0.4040404 | 0.31034482 | 0.71428573 | 0.31578946 | 0.45045045 | 0.47619048 | 0.33333334 | 0.3030303 | 0.5555556 | 0.5833333 | 0.47619048 | 0.33333334 | 0.75 | 0.46052632 | 0.5 | 0.39285713 | 0.5555556 | 0.36585367 | 0.36363637 | 0.3181818 | 0.48507464 | 0.45454547 | 0.5 | 0.28735632 | 0.703125 | 0.4651163 | 0.5194805 | 0.61538464 |
| 5 | f1 | 0.4475224 | 0.11853226 | 0.6086956 | 0.36363637 | 0.41025642 | 0.39130434 | 0.5882353 | 0.34285715 | 0.47619048 | 0.3809524 | 0.26666668 | 0.30769232 | 0.54545456 | 0.5185185 | 0.4848485 | 0.2962963 | 0.54545456 | 0.4516129 | 0.5 | 0.4680851 | 0.5714286 | 0.38709676 | 0.32 | 0.3181818 | 0.5531915 | 0.44444445 | 0.32 | 0.25641027 | 0.72 | 0.42105263 | 0.55172414 | 0.61538464 |
| 6 | f2 | 0.44655478 | 0.13771796 | 0.6603774 | 0.37037036 | 0.41666666 | 0.5294118 | 0.5 | 0.375 | 0.5050505 | 0.31746033 | 0.22222222 | 0.3125 | 0.53571427 | 0.46666667 | 0.49382716 | 0.26666668 | 0.42857143 | 0.443038 | 0.5 | 0.57894737 | 0.5882353 | 0.41095892 | 0.2857143 | 0.3181818 | 0.64356434 | 0.4347826 | 0.23529412 | 0.23148148 | 0.73770493 | 0.3846154 | 0.5882353 | 0.61538464 |
| 7 | lift_top_group | 9.418556 | 6.5104547 | 13.0 | 5.4166665 | 4.5614033 | 20.0 | 15.757576 | 5.7777777 | 0.0 | 12.380953 | 8.666667 | 9.122807 | 15.294118 | 10.833333 | 10.833333 | 10.833333 | 32.5 | 10.833333 | 7.1944447 | 5.3958335 | 8.633333 | 6.1666665 | 11.511111 | 3.9242425 | 4.796296 | 0.0 | 12.95 | 0.0 | 7.1944447 | 15.69697 | 6.6410255 | 6.6410255 |
| 8 | logloss | 0.18844378 | 0.042583752 | 0.18835595 | 0.20363313 | 0.23024575 | 0.16587171 | 0.1355959 | 0.19911318 | 0.2205489 | 0.18006141 | 0.15921076 | 0.23854852 | 0.17812935 | 0.1929188 | 0.18743399 | 0.21349046 | 0.115545526 | 0.19330409 | 0.15298136 | 0.18778694 | 0.1961473 | 0.18005897 | 0.20364355 | 0.27162477 | 0.18701267 | 0.18097323 | 0.25371486 | 0.2964184 | 0.11297 | 0.14982502 | 0.14446479 | 0.13368414 |
| 9 | max_per_class_error | 0.5462117 | 0.16337207 | 0.3 | 0.625 | 0.57894737 | 0.30769232 | 0.54545456 | 0.6 | 0.47368422 | 0.71428573 | 0.8 | 0.68421054 | 0.47058824 | 0.5625 | 0.5 | 0.75 | 0.625 | 0.5625 | 0.5 | 0.3125 | 0.4 | 0.5714286 | 0.73333335 | 0.6818182 | 0.2777778 | 0.5714286 | 0.8 | 0.7826087 | 0.25 | 0.6363636 | 0.3846154 | 0.3846154 |
| 10 | mcc | 0.42523345 | 0.12697941 | 0.57735026 | 0.3207254 | 0.36261266 | 0.38964638 | 0.6039857 | 0.3 | 0.43294537 | 0.3814258 | 0.26325265 | 0.25169587 | 0.5149535 | 0.5027486 | 0.4502108 | 0.26421827 | 0.60638624 | 0.417132 | 0.4757085 | 0.4488596 | 0.5344245 | 0.3503295 | 0.29349014 | 0.25489068 | 0.5289012 | 0.41425505 | 0.3798919 | 0.20178863 | 0.7064492 | 0.40504596 | 0.52861995 | 0.5950594 |
| 11 | mean_per_class_accuracy | 0.70864195 | 0.077748306 | 0.825 | 0.664959 | 0.68563 | 0.7975708 | 0.72526467 | 0.67142856 | 0.7361869 | 0.6367596 | 0.594 | 0.6288491 | 0.7503026 | 0.7105533 | 0.73155737 | 0.6106557 | 0.6875 | 0.7023566 | 0.73785424 | 0.80259776 | 0.7790795 | 0.6918367 | 0.62103826 | 0.62744534 | 0.8279161 | 0.7 | 0.59790796 | 0.58539057 | 0.8669028 | 0.6737537 | 0.79143214 | 0.7975297 |
| 12 | mean_per_class_error | 0.29135802 | 0.077748306 | 0.175 | 0.335041 | 0.31436995 | 0.20242915 | 0.2747353 | 0.32857144 | 0.26381305 | 0.36324042 | 0.406 | 0.3711509 | 0.24969742 | 0.2894467 | 0.26844263 | 0.38934427 | 0.3125 | 0.29764345 | 0.26214576 | 0.19740227 | 0.2209205 | 0.30816326 | 0.37896174 | 0.37255466 | 0.17208391 | 0.3 | 0.40209204 | 0.41460943 | 0.13309717 | 0.32624632 | 0.20856786 | 0.20247029 |
| 13 | mse | 0.048659742 | 0.01245876 | 0.05115184 | 0.052604154 | 0.060449596 | 0.04229647 | 0.032288227 | 0.05115559 | 0.05818293 | 0.04561904 | 0.036503375 | 0.06336227 | 0.04620266 | 0.048833713 | 0.0480811 | 0.054343026 | 0.026085114 | 0.04934566 | 0.03651688 | 0.049958732 | 0.05369695 | 0.045542303 | 0.053778086 | 0.07328628 | 0.050529182 | 0.045930754 | 0.06716473 | 0.07981833 | 0.028954051 | 0.036343593 | 0.03756328 | 0.034204308 |
| 14 | null_deviance | 118.026535 | 19.989788 | 142.31174 | 120.223526 | 136.7752 | 103.75808 | 92.82863 | 114.7255 | 136.7752 | 109.23703 | 87.37807 | 136.7752 | 125.73113 | 120.223526 | 120.223526 | 120.223526 | 76.50513 | 120.223526 | 98.16257 | 120.09978 | 142.19029 | 109.11213 | 114.60118 | 153.29364 | 131.12575 | 109.11213 | 142.19029 | 158.85994 | 98.16257 | 92.701996 | 103.63261 | 103.63261 |
| 15 | pr_auc | 0.31358626 | 0.101377964 | 0.46072215 | 0.18014134 | 0.2798099 | 0.38177586 | 0.46781328 | 0.1946858 | 0.27582237 | 0.25563782 | 0.187521 | 0.27700686 | 0.46827513 | 0.30929434 | 0.33145308 | 0.25819957 | 0.44160524 | 0.3139661 | 0.26947194 | 0.2970176 | 0.41927946 | 0.2594395 | 0.21610877 | 0.21034263 | 0.36788866 | 0.18535477 | 0.32192892 | 0.14440827 | 0.53039646 | 0.32945853 | 0.32843795 | 0.4443246 |
| 16 | precision | 0.49016175 | 0.17148666 | 0.53846157 | 0.3529412 | 0.4 | 0.27272728 | 0.8333333 | 0.3 | 0.4347826 | 0.5714286 | 0.4 | 0.3 | 0.5625 | 0.6363636 | 0.47058824 | 0.36363637 | 1.0 | 0.46666667 | 0.5 | 0.3548387 | 0.54545456 | 0.3529412 | 0.4 | 0.3181818 | 0.44827586 | 0.46153846 | 0.8 | 0.3125 | 0.6923077 | 0.5 | 0.5 | 0.61538464 |
| 17 | r2 | 0.1380893 | 0.08533912 | 0.27961162 | 0.08912891 | 0.107579686 | 0.109547995 | 0.20310907 | 0.059015576 | 0.14104259 | 0.10457398 | 0.012948798 | 0.0645797 | 0.24393615 | 0.15441626 | 0.1674482 | 0.059019335 | 0.12532057 | 0.14555156 | 0.17355306 | 0.13804483 | 0.24643408 | 0.10932268 | 0.014347573 | 0.057131406 | 0.21863808 | 0.10172567 | 0.057431597 | 0.013578965 | 0.34471434 | 0.106318 | 0.21207559 | 0.28253314 |
| 18 | recall | 0.45378825 | 0.16337207 | 0.7 | 0.375 | 0.42105263 | 0.6923077 | 0.45454547 | 0.4 | 0.5263158 | 0.2857143 | 0.2 | 0.31578946 | 0.5294118 | 0.4375 | 0.5 | 0.25 | 0.375 | 0.4375 | 0.5 | 0.6875 | 0.6 | 0.42857143 | 0.26666668 | 0.3181818 | 0.7222222 | 0.42857143 | 0.2 | 0.2173913 | 0.75 | 0.36363637 | 0.61538464 | 0.61538464 |
| 19 | residual_deviance | 97.81401 | 22.081083 | 97.9451 | 105.88923 | 119.72779 | 86.25329 | 70.50987 | 103.53886 | 114.685425 | 93.631935 | 82.7896 | 124.04523 | 92.62726 | 100.31778 | 97.465675 | 111.01504 | 60.08367 | 100.51813 | 79.24435 | 97.27363 | 101.6043 | 93.270546 | 105.48736 | 140.70163 | 96.87257 | 93.74413 | 131.4243 | 153.54474 | 58.518463 | 77.60936 | 74.832756 | 69.24839 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:14:15 | 0.000 sec | 2 | .81E1 | 15 | 0.452201 | 0.451530 | 0.452561 | 0.014031 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:14:15 | 0.004 sec | 4 | .5E1 | 15 | 0.450817 | 0.449794 | 0.451240 | 0.014002 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:14:15 | 0.008 sec | 6 | .31E1 | 15 | 0.448642 | 0.447065 | 0.449165 | 0.013959 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:14:15 | 0.011 sec | 8 | .19E1 | 15 | 0.445267 | 0.442823 | 0.445939 | 0.013897 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:14:15 | 0.014 sec | 10 | .12E1 | 15 | 0.440194 | 0.436427 | 0.441079 | 0.013815 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:14:15 | 0.017 sec | 12 | .75E0 | 15 | 0.432912 | 0.427204 | 0.434076 | 0.013722 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:14:15 | 0.021 sec | 14 | .47E0 | 15 | 0.423265 | 0.414900 | 0.424737 | 0.013655 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:14:15 | 0.024 sec | 16 | .29E0 | 15 | 0.412058 | 0.400454 | 0.413784 | 0.013678 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:14:15 | 0.027 sec | 18 | .18E0 | 15 | 0.401137 | 0.386170 | 0.403012 | 0.013849 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:14:15 | 0.031 sec | 20 | .11E0 | 15 | 0.392305 | 0.374427 | 0.394272 | 0.014152 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:14:15 | 0.039 sec | 22 | .69E-1 | 15 | 0.386131 | 0.366117 | 0.388225 | 0.014511 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:14:15 | 0.043 sec | 24 | .43E-1 | 15 | 0.382171 | 0.360832 | 0.384459 | 0.014851 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:14:15 | 0.046 sec | 26 | .27E-1 | 15 | 0.379697 | 0.357735 | 0.382233 | 0.015132 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:14:15 | 0.049 sec | 28 | .17E-1 | 15 | 0.378130 | 0.356088 | 0.380930 | 0.015347 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:14:15 | 0.053 sec | 30 | .1E-1 | 15 | 0.377123 | 0.355390 | 0.380426 | 0.015633 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:14:15 | 0.056 sec | 32 | .64E-2 | 15 | 0.376481 | 0.355283 | 0.380274 | 0.015706 | 0.0 | 32.0 | 0.220329 | 0.188044 | 0.14072 | 0.767535 | 0.282628 | 8.744959 | 0.068713 | 0.214827 | 0.177751 | 0.182901 | 0.788492 | 0.306499 | 8.320513 | 0.066256 | |
| 16 | 2021-07-15 20:14:15 | 0.060 sec | 34 | .4E-2 | 15 | 0.376089 | 0.355502 | 0.380010 | 0.015787 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:14:15 | 0.063 sec | 36 | .25E-2 | 15 | 0.375861 | 0.355855 | 0.383344 | 0.016496 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:14:15 | 0.065 sec | 37 | .15E-2 | 15 | 0.375734 | 0.356216 | 0.385128 | 0.016561 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:14:15 | 0.067 sec | 38 | .95E-3 | 15 | 0.375657 | 0.356526 | 0.399257 | 0.017745 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.544724 | 1.000000 | 0.225285 |
| 1 | Average_Transaction_Frequency | 0.296069 | 0.543521 | 0.122447 |
| 2 | Card_Type.1 | 0.278417 | 0.511117 | 0.115147 |
| 3 | Card_Type.0 | 0.273639 | 0.502344 | 0.113170 |
| 4 | Minimum_Transaction_Amount | 0.206425 | 0.378953 | 0.085372 |
| 5 | Merchant_ID | 0.186618 | 0.342591 | 0.077181 |
| 6 | Channel_ID | 0.174517 | 0.320376 | 0.072176 |
| 7 | Transaction_Amount | 0.140544 | 0.258009 | 0.058126 |
| 8 | Transaction_Date | 0.098974 | 0.181695 | 0.040933 |
| 9 | Maximum_Transaction_Amount | 0.096781 | 0.177670 | 0.040026 |
| 10 | Day | 0.070445 | 0.129322 | 0.029134 |
| 11 | Average_Transaction_Amount | 0.022903 | 0.042046 | 0.009472 |
| 12 | City_ID | 0.015839 | 0.029078 | 0.006551 |
| 13 | Month | 0.012041 | 0.022104 | 0.004980 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201419 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.00674 ) | nlambda = 30, lambda.max = 8.5525, lambda.min = 0.00674, lambda.1s... | 14 | 14 | 32 | automl_training_py_534_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.0483796349794075 RMSE: 0.2199537109925802 LogLoss: 0.1867933629636763 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793819 Residual deviance: 2908.7462480703675 AIC: 2938.7462480703675 AUC: 0.7731458609259921 AUCPR: 0.275848852508365 Gini: 0.5462917218519843 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.14254805938670914:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6958.0 | 360.0 | 0.0492 | (360.0/7318.0) |
| 1 | 1 | 256.0 | 212.0 | 0.547 | (256.0/468.0) |
| 2 | Total | 7214.0 | 572.0 | 0.0791 | (616.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.142548 | 0.407692 | 183.0 |
| 1 | max f2 | 0.058465 | 0.443841 | 237.0 |
| 2 | max f0point5 | 0.305581 | 0.407658 | 126.0 |
| 3 | max accuracy | 0.573988 | 0.940406 | 7.0 |
| 4 | max precision | 0.573988 | 0.750000 | 7.0 |
| 5 | max recall | 0.019774 | 1.000000 | 379.0 |
| 6 | max specificity | 0.841643 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.142548 | 0.367872 | 183.0 |
| 8 | max min_per_class_accuracy | 0.040974 | 0.694444 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.058465 | 0.717819 | 237.0 |
| 10 | max tns | 0.841643 | 7317.000000 | 0.0 |
| 11 | max fns | 0.841643 | 468.000000 | 0.0 |
| 12 | max fps | 0.001642 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019774 | 468.000000 | 379.0 |
| 14 | max tnr | 0.841643 | 0.999863 | 0.0 |
| 15 | max fnr | 0.841643 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001642 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019774 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.423619 | 7.891793 | 7.891793 | 0.474359 | 0.494601 | 0.474359 | 0.494601 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.392652 | 6.825334 | 7.358563 | 0.410256 | 0.406473 | 0.442308 | 0.450537 | 0.068376 | 0.147436 | 582.533421 | 635.856345 | 0.135547 |
| 2 | 3 | 0.030054 | 0.365755 | 6.612043 | 7.109723 | 0.397436 | 0.378494 | 0.427350 | 0.426523 | 0.066239 | 0.213675 | 561.204252 | 610.972314 | 0.195364 |
| 3 | 4 | 0.040072 | 0.347275 | 6.825334 | 7.038626 | 0.410256 | 0.355937 | 0.423077 | 0.408876 | 0.068376 | 0.282051 | 582.533421 | 603.862590 | 0.257454 |
| 4 | 5 | 0.050090 | 0.323556 | 5.972167 | 6.825334 | 0.358974 | 0.336067 | 0.410256 | 0.394315 | 0.059829 | 0.341880 | 497.216743 | 582.533421 | 0.310451 |
| 5 | 6 | 0.100051 | 0.064352 | 2.993760 | 4.912006 | 0.179949 | 0.171154 | 0.295250 | 0.282878 | 0.149573 | 0.491453 | 199.376002 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.050736 | 1.197504 | 3.674899 | 0.071979 | 0.055990 | 0.220890 | 0.207313 | 0.059829 | 0.551282 | 19.750401 | 267.489902 | 0.426931 |
| 7 | 8 | 0.200103 | 0.046369 | 0.682533 | 2.925847 | 0.041026 | 0.048262 | 0.175866 | 0.167499 | 0.034188 | 0.585470 | -31.746658 | 192.584729 | 0.410012 |
| 8 | 9 | 0.300026 | 0.041759 | 0.898128 | 2.250520 | 0.053985 | 0.043829 | 0.135274 | 0.126311 | 0.089744 | 0.675214 | -10.187199 | 125.051955 | 0.399182 |
| 9 | 10 | 0.400077 | 0.038705 | 0.619340 | 1.842594 | 0.037227 | 0.040159 | 0.110754 | 0.104766 | 0.061966 | 0.737179 | -38.066006 | 84.259374 | 0.358661 |
| 10 | 11 | 0.500000 | 0.036124 | 0.641520 | 1.602564 | 0.038560 | 0.037385 | 0.096327 | 0.091300 | 0.064103 | 0.801282 | -35.847999 | 60.256410 | 0.320550 |
| 11 | 12 | 0.600051 | 0.033903 | 0.747479 | 1.459989 | 0.044929 | 0.034997 | 0.087757 | 0.081913 | 0.074786 | 0.876068 | -25.252076 | 45.998895 | 0.293669 |
| 12 | 13 | 0.699974 | 0.031402 | 0.406296 | 1.309572 | 0.024422 | 0.032678 | 0.078716 | 0.074884 | 0.040598 | 0.916667 | -59.370400 | 30.957187 | 0.230550 |
| 13 | 14 | 0.800026 | 0.028719 | 0.277635 | 1.180518 | 0.016688 | 0.030135 | 0.070958 | 0.069288 | 0.027778 | 0.944444 | -72.236486 | 18.051765 | 0.153655 |
| 14 | 15 | 0.899949 | 0.024528 | 0.320760 | 1.085057 | 0.019280 | 0.026837 | 0.065220 | 0.064574 | 0.032051 | 0.976496 | -67.924000 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.001299 | 0.234922 | 1.000000 | 0.014121 | 0.019932 | 0.060108 | 0.060108 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.046961970819690044 RMSE: 0.2167071083736988 LogLoss: 0.18385347391038964 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311502 Residual deviance: 715.9254274070573 AIC: 745.9254274070573 AUC: 0.7654383260940638 AUCPR: 0.3457565221132125 Gini: 0.5308766521881276 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.3335160212135909:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1788.0 | 42.0 | 0.023 | (42.0/1830.0) |
| 1 | 1 | 71.0 | 46.0 | 0.6068 | (71.0/117.0) |
| 2 | Total | 1859.0 | 88.0 | 0.058 | (113.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.333516 | 0.448780 | 74.0 |
| 1 | max f2 | 0.071451 | 0.453100 | 135.0 |
| 2 | max f0point5 | 0.360147 | 0.498534 | 49.0 |
| 3 | max accuracy | 0.385591 | 0.946584 | 36.0 |
| 4 | max precision | 0.513702 | 0.833333 | 4.0 |
| 5 | max recall | 0.022900 | 1.000000 | 358.0 |
| 6 | max specificity | 0.838605 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.333516 | 0.423533 | 74.0 |
| 8 | max min_per_class_accuracy | 0.040592 | 0.666667 | 236.0 |
| 9 | max mean_per_class_accuracy | 0.071451 | 0.715174 | 135.0 |
| 10 | max tns | 0.838605 | 1829.000000 | 0.0 |
| 11 | max fns | 0.838605 | 116.000000 | 0.0 |
| 12 | max fps | 0.001545 | 1830.000000 | 399.0 |
| 13 | max tps | 0.022900 | 117.000000 | 358.0 |
| 14 | max tnr | 0.838605 | 0.999454 | 0.0 |
| 15 | max fnr | 0.838605 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001545 | 1.000000 | 399.0 |
| 17 | max tpr | 0.022900 | 1.000000 | 358.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.77 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.429456 | 11.648718 | 11.648718 | 0.700000 | 0.514090 | 0.700000 | 0.514090 | 0.119658 | 0.119658 | 1064.871795 | 1064.871795 | 0.116379 |
| 1 | 2 | 0.020031 | 0.381059 | 10.510121 | 11.094017 | 0.631579 | 0.406531 | 0.666667 | 0.461690 | 0.102564 | 0.222222 | 951.012146 | 1009.401709 | 0.215118 |
| 2 | 3 | 0.030303 | 0.356834 | 6.656410 | 9.589744 | 0.400000 | 0.368242 | 0.576271 | 0.430013 | 0.068376 | 0.290598 | 565.641026 | 858.974359 | 0.276937 |
| 3 | 4 | 0.040062 | 0.343386 | 7.006748 | 8.960552 | 0.421053 | 0.349185 | 0.538462 | 0.410324 | 0.068376 | 0.358974 | 600.674764 | 796.055227 | 0.339302 |
| 4 | 5 | 0.050334 | 0.310886 | 3.328205 | 7.811094 | 0.200000 | 0.330438 | 0.469388 | 0.394021 | 0.034188 | 0.393162 | 232.820513 | 681.109367 | 0.364747 |
| 5 | 6 | 0.100154 | 0.060226 | 2.058684 | 4.949638 | 0.123711 | 0.127360 | 0.297436 | 0.261374 | 0.102564 | 0.495726 | 105.868358 | 394.963840 | 0.420863 |
| 6 | 7 | 0.149974 | 0.049716 | 0.514671 | 3.476379 | 0.030928 | 0.054049 | 0.208904 | 0.192503 | 0.025641 | 0.521368 | -48.532910 | 247.637864 | 0.395138 |
| 7 | 8 | 0.200308 | 0.045711 | 1.018838 | 2.858843 | 0.061224 | 0.047591 | 0.171795 | 0.156089 | 0.051282 | 0.572650 | 1.883830 | 185.884287 | 0.396147 |
| 8 | 9 | 0.299949 | 0.041698 | 0.772006 | 2.165613 | 0.046392 | 0.043496 | 0.130137 | 0.118686 | 0.076923 | 0.649573 | -22.799366 | 116.561293 | 0.371977 |
| 9 | 10 | 0.400103 | 0.038574 | 0.597370 | 1.773049 | 0.035897 | 0.040205 | 0.106547 | 0.099041 | 0.059829 | 0.709402 | -40.262985 | 77.304895 | 0.329074 |
| 10 | 11 | 0.500257 | 0.036045 | 0.682709 | 1.554757 | 0.041026 | 0.037268 | 0.093429 | 0.086674 | 0.068376 | 0.777778 | -31.729126 | 55.475702 | 0.295264 |
| 11 | 12 | 0.599897 | 0.033618 | 0.943563 | 1.453240 | 0.056701 | 0.034881 | 0.087329 | 0.078071 | 0.094017 | 0.871795 | -5.643669 | 45.324025 | 0.289281 |
| 12 | 13 | 0.700051 | 0.031388 | 0.170677 | 1.269748 | 0.010256 | 0.032497 | 0.076302 | 0.071551 | 0.017094 | 0.888889 | -82.932281 | 26.974810 | 0.200911 |
| 13 | 14 | 0.799692 | 0.028419 | 0.600449 | 1.186354 | 0.036082 | 0.030015 | 0.071291 | 0.066376 | 0.059829 | 0.948718 | -39.955062 | 18.635443 | 0.158554 |
| 14 | 15 | 0.899846 | 0.024297 | 0.256016 | 1.082806 | 0.015385 | 0.026443 | 0.065068 | 0.061931 | 0.025641 | 0.974359 | -74.398422 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.001502 | 0.256016 | 1.000000 | 0.015385 | 0.019340 | 0.060092 | 0.057665 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04862744585116121 RMSE: 0.22051631651912112 LogLoss: 0.1883923536607146 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.064043047757 Residual deviance: 2933.6457312046477 AIC: 2963.6457312046477 AUC: 0.7569378747637835 AUCPR: 0.2641284916075208 Gini: 0.513875749527567 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.12271312077388102:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6963.0 | 355.0 | 0.0485 | (355.0/7318.0) |
| 1 | 1 | 257.0 | 211.0 | 0.5491 | (257.0/468.0) |
| 2 | Total | 7220.0 | 566.0 | 0.0786 | (612.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.122713 | 0.408124 | 192.0 |
| 1 | max f2 | 0.061465 | 0.435435 | 233.0 |
| 2 | max f0point5 | 0.287108 | 0.403846 | 141.0 |
| 3 | max accuracy | 0.550662 | 0.940406 | 9.0 |
| 4 | max precision | 0.582911 | 0.714286 | 5.0 |
| 5 | max recall | 0.019402 | 1.000000 | 379.0 |
| 6 | max specificity | 0.862326 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.122713 | 0.368332 | 192.0 |
| 8 | max min_per_class_accuracy | 0.041240 | 0.679487 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.061465 | 0.709601 | 233.0 |
| 10 | max tns | 0.862326 | 7317.000000 | 0.0 |
| 11 | max fns | 0.862326 | 468.000000 | 0.0 |
| 12 | max fps | 0.001652 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019402 | 468.000000 | 379.0 |
| 14 | max tnr | 0.862326 | 0.999863 | 0.0 |
| 15 | max fnr | 0.862326 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001652 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019402 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.02 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.423353 | 7.251918 | 7.251918 | 0.435897 | 0.494074 | 0.435897 | 0.494074 | 0.072650 | 0.072650 | 625.191760 | 625.191760 | 0.066637 |
| 1 | 2 | 0.020036 | 0.387029 | 7.891793 | 7.571855 | 0.474359 | 0.404057 | 0.455128 | 0.449066 | 0.079060 | 0.151709 | 689.179268 | 657.185514 | 0.140094 |
| 2 | 3 | 0.030054 | 0.361905 | 5.758876 | 6.967529 | 0.346154 | 0.374142 | 0.418803 | 0.424091 | 0.057692 | 0.209402 | 475.887574 | 596.752867 | 0.190817 |
| 3 | 4 | 0.040072 | 0.343940 | 6.825334 | 6.931980 | 0.410256 | 0.352630 | 0.416667 | 0.406226 | 0.068376 | 0.277778 | 582.533421 | 593.198006 | 0.252908 |
| 4 | 5 | 0.050090 | 0.318572 | 5.972167 | 6.740018 | 0.358974 | 0.332335 | 0.405128 | 0.391448 | 0.059829 | 0.337607 | 497.216743 | 574.001753 | 0.305904 |
| 5 | 6 | 0.100051 | 0.062175 | 3.079296 | 4.912006 | 0.185090 | 0.160072 | 0.295250 | 0.275908 | 0.153846 | 0.491453 | 207.929603 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.056063 | 0.855360 | 3.560949 | 0.051414 | 0.060198 | 0.214041 | 0.204067 | 0.042735 | 0.534188 | -14.463999 | 256.094866 | 0.408744 |
| 7 | 8 | 0.200103 | 0.048360 | 0.810508 | 2.872456 | 0.048718 | 0.051569 | 0.172657 | 0.165893 | 0.040598 | 0.574786 | -18.949156 | 187.245592 | 0.398645 |
| 8 | 9 | 0.300026 | 0.042602 | 0.791208 | 2.179301 | 0.047558 | 0.045123 | 0.130993 | 0.125671 | 0.079060 | 0.653846 | -20.879199 | 117.930058 | 0.376448 |
| 9 | 10 | 0.400077 | 0.039277 | 0.555270 | 1.773163 | 0.033376 | 0.040819 | 0.106581 | 0.104451 | 0.055556 | 0.709402 | -44.472971 | 77.316267 | 0.329107 |
| 10 | 11 | 0.500000 | 0.036534 | 0.491832 | 1.517094 | 0.029563 | 0.037870 | 0.091189 | 0.091145 | 0.049145 | 0.758547 | -50.816800 | 51.709402 | 0.275082 |
| 11 | 12 | 0.600051 | 0.034245 | 0.768836 | 1.392331 | 0.046213 | 0.035342 | 0.083690 | 0.081841 | 0.076923 | 0.835470 | -23.116421 | 39.233093 | 0.250474 |
| 12 | 13 | 0.699974 | 0.031663 | 0.684288 | 1.291256 | 0.041131 | 0.032958 | 0.077615 | 0.074862 | 0.068376 | 0.903846 | -31.571199 | 29.125618 | 0.216910 |
| 13 | 14 | 0.800026 | 0.028933 | 0.320348 | 1.169834 | 0.019255 | 0.030372 | 0.070316 | 0.069298 | 0.032051 | 0.935897 | -67.965176 | 16.983423 | 0.144561 |
| 14 | 15 | 0.899949 | 0.024800 | 0.363528 | 1.080309 | 0.021851 | 0.027118 | 0.064935 | 0.064615 | 0.036325 | 0.972222 | -63.647200 | 8.030858 | 0.076896 |
| 15 | 16 | 1.000000 | 0.001411 | 0.277635 | 1.000000 | 0.016688 | 0.020246 | 0.060108 | 0.060176 | 0.027778 | 1.000000 | -72.236486 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9330873 | 0.01789851 | 0.95 | 0.9153846 | 0.9423077 | 0.9 | 0.9307692 | 0.96153843 | 0.90384614 | 0.96153843 | 0.9461538 | 0.9115385 | 0.9230769 | 0.91923076 | 0.9461538 | 0.9230769 | 0.95384616 | 0.9230769 | 0.9034749 | 0.95366794 | 0.94208497 | 0.93436295 | 0.9227799 | 0.96138996 | 0.93436295 | 0.9111969 | 0.9459459 | 0.94208497 | 0.93436295 | 0.9189189 | 0.94208497 | 0.93436295 |
| 1 | auc | 0.763139 | 0.05533689 | 0.7537747 | 0.7129252 | 0.77708906 | 0.7912558 | 0.7505123 | 0.7916537 | 0.79333335 | 0.73932093 | 0.7794723 | 0.74966395 | 0.7559913 | 0.77124184 | 0.6869877 | 0.71618855 | 0.8051684 | 0.6427623 | 0.73144305 | 0.74320173 | 0.7798624 | 0.8273878 | 0.64446527 | 0.8441235 | 0.7957819 | 0.83103186 | 0.88742965 | 0.68218625 | 0.7672131 | 0.7739541 | 0.8215768 | 0.7471708 |
| 2 | err | 0.06691268 | 0.01789851 | 0.05 | 0.08461539 | 0.057692308 | 0.1 | 0.06923077 | 0.03846154 | 0.09615385 | 0.03846154 | 0.053846154 | 0.08846154 | 0.07692308 | 0.08076923 | 0.053846154 | 0.07692308 | 0.046153847 | 0.07692308 | 0.096525095 | 0.046332046 | 0.057915058 | 0.06563707 | 0.077220075 | 0.038610037 | 0.06563707 | 0.08880309 | 0.054054055 | 0.057915058 | 0.06563707 | 0.08108108 | 0.057915058 | 0.06563707 |
| 3 | err_count | 17.366667 | 4.6496816 | 13.0 | 22.0 | 15.0 | 26.0 | 18.0 | 10.0 | 25.0 | 10.0 | 14.0 | 23.0 | 20.0 | 21.0 | 14.0 | 20.0 | 12.0 | 20.0 | 25.0 | 12.0 | 15.0 | 17.0 | 20.0 | 10.0 | 17.0 | 23.0 | 14.0 | 15.0 | 17.0 | 21.0 | 15.0 | 17.0 |
| 4 | f0point5 | 0.4528783 | 0.12258877 | 0.530303 | 0.37383178 | 0.5813953 | 0.5 | 0.45454547 | 0.61403507 | 0.3846154 | 0.48387095 | 0.57377046 | 0.26041666 | 0.38961038 | 0.44247788 | 0.53571427 | 0.375 | 0.5813953 | 0.19480519 | 0.34313726 | 0.7446808 | 0.3773585 | 0.6111111 | 0.2739726 | 0.41666666 | 0.47619048 | 0.5 | 0.46153846 | 0.35714287 | 0.4597701 | 0.2840909 | 0.5882353 | 0.41666666 |
| 5 | f1 | 0.44486135 | 0.10187917 | 0.5185185 | 0.42105263 | 0.5714286 | 0.5 | 0.47058824 | 0.5833333 | 0.3902439 | 0.375 | 0.5 | 0.3030303 | 0.375 | 0.4878049 | 0.46153846 | 0.375 | 0.625 | 0.23076923 | 0.35897437 | 0.53846157 | 0.3478261 | 0.5641026 | 0.2857143 | 0.44444445 | 0.4848485 | 0.53061223 | 0.46153846 | 0.3478261 | 0.4848485 | 0.32258064 | 0.61538464 | 0.37037036 |
| 6 | f2 | 0.44630033 | 0.1028163 | 0.5072464 | 0.48192772 | 0.56179774 | 0.5 | 0.4878049 | 0.5555556 | 0.3960396 | 0.30612245 | 0.443038 | 0.36231884 | 0.36144578 | 0.54347825 | 0.4054054 | 0.375 | 0.6756757 | 0.28301886 | 0.37634408 | 0.42168674 | 0.32258064 | 0.52380955 | 0.29850745 | 0.47619048 | 0.49382716 | 0.5652174 | 0.46153846 | 0.33898306 | 0.51282054 | 0.37313432 | 0.6451613 | 0.33333334 |
| 7 | lift_top_group | 7.2002506 | 4.036126 | 12.380953 | 0.0 | 9.62963 | 6.6666665 | 5.4166665 | 6.6666665 | 4.3333335 | 7.878788 | 5.098039 | 14.444445 | 10.196078 | 0.0 | 5.4166665 | 0.0 | 6.1904764 | 0.0 | 4.796296 | 13.631579 | 6.6410255 | 11.772727 | 6.6410255 | 10.791667 | 5.3958335 | 7.848485 | 13.282051 | 7.1944447 | 11.511111 | 7.1944447 | 9.592592 | 5.3958335 |
| 8 | logloss | 0.18697897 | 0.03271071 | 0.16523673 | 0.18694808 | 0.19014728 | 0.27851355 | 0.19698551 | 0.14229092 | 0.23827453 | 0.15667398 | 0.18992089 | 0.17204833 | 0.20924784 | 0.19408944 | 0.19996041 | 0.20979533 | 0.15294202 | 0.14828262 | 0.22790207 | 0.20390302 | 0.17655995 | 0.21760377 | 0.18502249 | 0.113298304 | 0.18610321 | 0.22194806 | 0.15435041 | 0.16753308 | 0.17953135 | 0.16543564 | 0.1699096 | 0.20891048 |
| 9 | max_per_class_error | 0.5481684 | 0.11125396 | 0.5 | 0.46666667 | 0.44444445 | 0.5 | 0.5 | 0.46153846 | 0.6 | 0.72727275 | 0.5882353 | 0.5833333 | 0.64705884 | 0.4117647 | 0.625 | 0.625 | 0.2857143 | 0.6666667 | 0.6111111 | 0.6315789 | 0.6923077 | 0.5 | 0.6923077 | 0.5 | 0.5 | 0.4090909 | 0.53846157 | 0.6666667 | 0.46666667 | 0.5833333 | 0.33333334 | 0.6875 |
| 10 | mcc | 0.41726485 | 0.1077541 | 0.49256343 | 0.3876437 | 0.54078156 | 0.44444445 | 0.43454942 | 0.5654795 | 0.33820412 | 0.38795996 | 0.4854702 | 0.27116662 | 0.33493015 | 0.45315975 | 0.4481291 | 0.33401638 | 0.6062093 | 0.20524937 | 0.30818322 | 0.5923489 | 0.3210623 | 0.534389 | 0.24581943 | 0.42753962 | 0.45006707 | 0.48521978 | 0.43308318 | 0.31791216 | 0.45224053 | 0.2902304 | 0.5862998 | 0.34359336 |
| 11 | mean_per_class_accuracy | 0.7075859 | 0.0545287 | 0.7378049 | 0.7360544 | 0.76331496 | 0.7222222 | 0.7295082 | 0.7611336 | 0.67291665 | 0.6323476 | 0.6976519 | 0.6760753 | 0.6579521 | 0.76531106 | 0.6793033 | 0.6670082 | 0.8408827 | 0.6387782 | 0.6653988 | 0.68421054 | 0.64165103 | 0.73734176 | 0.63148844 | 0.73804784 | 0.7314815 | 0.7659187 | 0.7165416 | 0.65249664 | 0.7461749 | 0.6799933 | 0.81466115 | 0.6439043 |
| 12 | mean_per_class_error | 0.29241416 | 0.0545287 | 0.2621951 | 0.26394558 | 0.23668504 | 0.2777778 | 0.2704918 | 0.2388664 | 0.32708332 | 0.36765242 | 0.3023481 | 0.32392472 | 0.34204793 | 0.23468894 | 0.3206967 | 0.3329918 | 0.15911731 | 0.3612218 | 0.3346012 | 0.31578946 | 0.35834897 | 0.26265824 | 0.36851156 | 0.2619522 | 0.2685185 | 0.23408131 | 0.2834584 | 0.34750336 | 0.25382513 | 0.32000676 | 0.18533887 | 0.35609567 |
| 13 | mse | 0.048256896 | 0.010205562 | 0.041673016 | 0.04873749 | 0.049550306 | 0.07745897 | 0.050982684 | 0.035274073 | 0.064458184 | 0.038145695 | 0.04864265 | 0.042418282 | 0.05373916 | 0.052374344 | 0.050415244 | 0.055530973 | 0.04029891 | 0.03338724 | 0.060640592 | 0.05273945 | 0.044258345 | 0.05879255 | 0.045371298 | 0.026392369 | 0.049265936 | 0.060438834 | 0.039505657 | 0.040891353 | 0.04630954 | 0.040990926 | 0.045531835 | 0.053491022 |
| 14 | null_deviance | 118.03547 | 21.437069 | 109.23703 | 114.7255 | 131.24835 | 175.73576 | 120.223526 | 103.75808 | 142.31174 | 92.82863 | 125.73113 | 98.28862 | 125.73113 | 125.73113 | 120.223526 | 120.223526 | 109.23703 | 81.936905 | 131.12575 | 136.65318 | 103.63261 | 153.29364 | 103.63261 | 76.37677 | 120.09978 | 153.29364 | 103.63261 | 98.16257 | 114.60118 | 98.16257 | 131.12575 | 120.09978 |
| 15 | pr_auc | 0.28626966 | 0.1117713 | 0.27547255 | 0.17400746 | 0.41691667 | 0.38727188 | 0.31313547 | 0.3855836 | 0.27445355 | 0.187927 | 0.35914174 | 0.24373205 | 0.2875301 | 0.23226559 | 0.26014873 | 0.16237035 | 0.3492845 | 0.07499235 | 0.20934293 | 0.51869047 | 0.20783968 | 0.50141925 | 0.12030581 | 0.20696256 | 0.2907793 | 0.46403822 | 0.30481482 | 0.15517092 | 0.33657604 | 0.19617425 | 0.4298639 | 0.2618784 |
| 16 | precision | 0.4664105 | 0.16011028 | 0.53846157 | 0.3478261 | 0.5882353 | 0.5 | 0.44444445 | 0.6363636 | 0.3809524 | 0.6 | 0.6363636 | 0.23809524 | 0.4 | 0.41666666 | 0.6 | 0.375 | 0.5555556 | 0.1764706 | 0.33333334 | 1.0 | 0.4 | 0.64705884 | 0.26666668 | 0.4 | 0.47058824 | 0.4814815 | 0.46153846 | 0.36363637 | 0.44444445 | 0.2631579 | 0.5714286 | 0.45454547 |
| 17 | r2 | 0.1347932 | 0.07568745 | 0.18202794 | 0.103495464 | 0.2310375 | 0.1393448 | 0.11720556 | 0.25738797 | 0.09221389 | 0.058543626 | 0.20400792 | 0.03646646 | 0.12060828 | 0.1429422 | 0.1270311 | 0.03844935 | 0.2089993 | 8.953302E-4 | 0.06227948 | 0.22416335 | 0.07164038 | 0.24360123 | 0.048295155 | 0.11831351 | 0.14999788 | 0.22242089 | 0.17133239 | 0.074550316 | 0.1512322 | 0.07229683 | 0.29591492 | 0.07710099 |
| 18 | recall | 0.45183155 | 0.11125396 | 0.5 | 0.53333336 | 0.5555556 | 0.5 | 0.5 | 0.53846157 | 0.4 | 0.27272728 | 0.4117647 | 0.41666666 | 0.3529412 | 0.5882353 | 0.375 | 0.375 | 0.71428573 | 0.33333334 | 0.3888889 | 0.36842105 | 0.30769232 | 0.5 | 0.30769232 | 0.5 | 0.5 | 0.59090906 | 0.46153846 | 0.33333334 | 0.53333336 | 0.41666666 | 0.6666667 | 0.3125 |
| 19 | residual_deviance | 97.05719 | 17.00015 | 85.923096 | 97.213 | 98.87659 | 144.82706 | 102.432465 | 73.99128 | 123.902756 | 81.470474 | 98.75886 | 89.46513 | 108.80888 | 100.92651 | 103.979416 | 109.09357 | 79.52985 | 77.106964 | 118.05327 | 105.621765 | 91.45806 | 112.71875 | 95.841644 | 58.688522 | 96.40146 | 114.96909 | 79.953514 | 86.782135 | 92.99724 | 85.69566 | 88.013176 | 108.21563 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:14:27 | 0.000 sec | 2 | .86E1 | 15 | 0.452140 | 0.452079 | 0.452519 | 0.014948 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:14:27 | 0.003 sec | 4 | .53E1 | 15 | 0.450718 | 0.450669 | 0.451161 | 0.014871 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:14:27 | 0.006 sec | 6 | .33E1 | 15 | 0.448482 | 0.448451 | 0.449025 | 0.014751 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:14:27 | 0.009 sec | 8 | .2E1 | 15 | 0.445006 | 0.445000 | 0.445700 | 0.014564 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:14:27 | 0.011 sec | 10 | .13E1 | 15 | 0.439766 | 0.439788 | 0.440676 | 0.014286 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:14:27 | 0.014 sec | 12 | .79E0 | 15 | 0.432213 | 0.432249 | 0.433403 | 0.013892 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:14:27 | 0.016 sec | 14 | .49E0 | 15 | 0.422141 | 0.422135 | 0.423642 | 0.013381 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:14:27 | 0.019 sec | 16 | .3E0 | 15 | 0.410337 | 0.410152 | 0.412089 | 0.012813 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:14:27 | 0.022 sec | 18 | .19E0 | 15 | 0.398742 | 0.398156 | 0.400613 | 0.012308 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:14:27 | 0.024 sec | 20 | .12E0 | 15 | 0.389338 | 0.388099 | 0.391257 | 0.011972 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:14:27 | 0.027 sec | 22 | .73E-1 | 15 | 0.382833 | 0.380751 | 0.384825 | 0.011818 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:14:27 | 0.030 sec | 24 | .45E-1 | 15 | 0.378768 | 0.375758 | 0.380918 | 0.011787 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:14:27 | 0.032 sec | 26 | .28E-1 | 15 | 0.376355 | 0.372437 | 0.378731 | 0.011814 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:14:27 | 0.035 sec | 28 | .17E-1 | 15 | 0.374931 | 0.370206 | 0.377568 | 0.011856 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:14:27 | 0.037 sec | 30 | .11E-1 | 15 | 0.374087 | 0.368703 | 0.376984 | 0.011891 | 0.0 | 30.0 | 0.219954 | 0.186793 | 0.143646 | 0.773146 | 0.275849 | 7.891793 | 0.079116 | 0.216707 | 0.183853 | 0.16854 | 0.765438 | 0.345757 | 11.648718 | 0.058038 | |
| 15 | 2021-07-15 20:14:27 | 0.040 sec | 32 | .67E-2 | 15 | 0.373587 | 0.367707 | 0.376775 | 0.011906 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:14:27 | 0.043 sec | 34 | .42E-2 | 15 | 0.373300 | 0.367070 | 0.378713 | 0.012386 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:14:27 | 0.044 sec | 35 | .26E-2 | 15 | 0.373146 | 0.366677 | 0.382196 | 0.013424 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:14:27 | 0.046 sec | 36 | .16E-2 | 15 | 0.373066 | 0.366438 | 0.385793 | 0.013459 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.557623 | 1.000000 | 0.263956 |
| 1 | Average_Transaction_Frequency | 0.228732 | 0.410191 | 0.108272 |
| 2 | Merchant_ID | 0.188023 | 0.337187 | 0.089002 |
| 3 | Channel_ID | 0.185515 | 0.332690 | 0.087815 |
| 4 | Minimum_Transaction_Amount | 0.184728 | 0.331278 | 0.087443 |
| 5 | Card_Type.1 | 0.180670 | 0.324000 | 0.085522 |
| 6 | Card_Type.0 | 0.178103 | 0.319396 | 0.084306 |
| 7 | Transaction_Amount | 0.110374 | 0.197937 | 0.052246 |
| 8 | Maximum_Transaction_Amount | 0.066082 | 0.118508 | 0.031281 |
| 9 | Day | 0.063970 | 0.114719 | 0.030281 |
| 10 | Transaction_Date | 0.063449 | 0.113785 | 0.030034 |
| 11 | Month | 0.044345 | 0.079525 | 0.020991 |
| 12 | Average_Transaction_Amount | 0.036656 | 0.065736 | 0.017351 |
| 13 | City_ID | 0.024293 | 0.043565 | 0.011499 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201430 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.004141 ) | nlambda = 30, lambda.max = 8.4612, lambda.min = 0.004141, lambda.1... | 14 | 14 | 34 | automl_training_py_569_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04798576375296462 RMSE: 0.219056530952548 LogLoss: 0.18552396131677715 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793838 Residual deviance: 2888.979125624854 AIC: 2918.979125624854 AUC: 0.7727305111153157 AUCPR: 0.2931741099161094 Gini: 0.5454610222306313 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.306338032879697:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7076.0 | 242.0 | 0.0331 | (242.0/7318.0) |
| 1 | 1 | 281.0 | 187.0 | 0.6004 | (281.0/468.0) |
| 2 | Total | 7357.0 | 429.0 | 0.0672 | (523.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.306338 | 0.416945 | 127.0 |
| 1 | max f2 | 0.077067 | 0.444915 | 221.0 |
| 2 | max f0point5 | 0.338765 | 0.430649 | 106.0 |
| 3 | max accuracy | 0.451517 | 0.940663 | 43.0 |
| 4 | max precision | 0.877907 | 1.000000 | 0.0 |
| 5 | max recall | 0.016298 | 1.000000 | 382.0 |
| 6 | max specificity | 0.877907 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.306338 | 0.381784 | 127.0 |
| 8 | max min_per_class_accuracy | 0.041355 | 0.690171 | 289.0 |
| 9 | max mean_per_class_accuracy | 0.060326 | 0.718198 | 241.0 |
| 10 | max tns | 0.877907 | 7318.000000 | 0.0 |
| 11 | max fns | 0.877907 | 467.000000 | 0.0 |
| 12 | max fps | 0.001005 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016298 | 468.000000 | 382.0 |
| 14 | max tnr | 0.877907 | 1.000000 | 0.0 |
| 15 | max fnr | 0.877907 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001005 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016298 | 1.000000 | 382.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.443261 | 8.744959 | 8.744959 | 0.525641 | 0.531398 | 0.525641 | 0.531398 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.397132 | 7.251918 | 7.998439 | 0.435897 | 0.417049 | 0.480769 | 0.474224 | 0.072650 | 0.160256 | 625.191760 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.368005 | 7.038626 | 7.678501 | 0.423077 | 0.383758 | 0.461538 | 0.444068 | 0.070513 | 0.230769 | 603.862590 | 667.850099 | 0.213551 |
| 3 | 4 | 0.040072 | 0.345904 | 7.891793 | 7.731824 | 0.474359 | 0.356015 | 0.464744 | 0.422055 | 0.079060 | 0.309829 | 689.179268 | 673.182391 | 0.287009 |
| 4 | 5 | 0.050090 | 0.319234 | 5.758876 | 7.337234 | 0.346154 | 0.332347 | 0.441026 | 0.404113 | 0.057692 | 0.367521 | 475.887574 | 633.723428 | 0.337732 |
| 5 | 6 | 0.100051 | 0.071133 | 2.608848 | 4.976076 | 0.156812 | 0.173877 | 0.299101 | 0.289143 | 0.130342 | 0.497863 | 160.884802 | 397.607606 | 0.423253 |
| 6 | 7 | 0.150013 | 0.053980 | 1.069200 | 3.674899 | 0.064267 | 0.060437 | 0.220890 | 0.212973 | 0.053419 | 0.551282 | 6.920001 | 267.489902 | 0.426931 |
| 7 | 8 | 0.200103 | 0.048184 | 0.938483 | 2.989917 | 0.056410 | 0.050654 | 0.179718 | 0.172341 | 0.047009 | 0.598291 | -6.151655 | 198.991694 | 0.423653 |
| 8 | 9 | 0.300026 | 0.042497 | 0.684288 | 2.222032 | 0.041131 | 0.045021 | 0.133562 | 0.129937 | 0.068376 | 0.666667 | -31.571199 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.038640 | 0.662053 | 1.831912 | 0.039795 | 0.040468 | 0.110112 | 0.107563 | 0.066239 | 0.732906 | -33.794696 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.035554 | 0.598752 | 1.585470 | 0.035990 | 0.037061 | 0.095299 | 0.093473 | 0.059829 | 0.792735 | -40.124800 | 58.547009 | 0.311456 |
| 11 | 12 | 0.600051 | 0.032553 | 0.747479 | 1.445745 | 0.044929 | 0.034026 | 0.086901 | 0.083561 | 0.074786 | 0.867521 | -25.252076 | 44.574516 | 0.284575 |
| 12 | 13 | 0.699974 | 0.029570 | 0.384912 | 1.294309 | 0.023136 | 0.031097 | 0.077798 | 0.076072 | 0.038462 | 0.905983 | -61.508800 | 29.430879 | 0.219183 |
| 13 | 14 | 0.800026 | 0.026160 | 0.427131 | 1.185859 | 0.025674 | 0.027926 | 0.071279 | 0.070051 | 0.042735 | 0.948718 | -57.286901 | 18.585936 | 0.158201 |
| 14 | 15 | 0.899949 | 0.021388 | 0.299376 | 1.087431 | 0.017995 | 0.023965 | 0.065363 | 0.064934 | 0.029915 | 0.978632 | -70.062400 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000653 | 0.213565 | 1.000000 | 0.012837 | 0.016700 | 0.060108 | 0.060108 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.048454141506693846 RMSE: 0.2201230144866589 LogLoss: 0.18741677816191585 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311521 Residual deviance: 729.8009341625003 AIC: 759.8009341625003 AUC: 0.7686212694409416 AUCPR: 0.2720736182134857 Gini: 0.5372425388818831 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13576992495937026:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1753.0 | 77.0 | 0.0421 | (77.0/1830.0) |
| 1 | 1 | 65.0 | 52.0 | 0.5556 | (65.0/117.0) |
| 2 | Total | 1818.0 | 129.0 | 0.0729 | (142.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.135770 | 0.422764 | 112.0 |
| 1 | max f2 | 0.135770 | 0.435511 | 112.0 |
| 2 | max f0point5 | 0.258224 | 0.412926 | 95.0 |
| 3 | max accuracy | 0.479815 | 0.940935 | 9.0 |
| 4 | max precision | 0.715010 | 1.000000 | 0.0 |
| 5 | max recall | 0.014530 | 1.000000 | 383.0 |
| 6 | max specificity | 0.715010 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.135770 | 0.384459 | 112.0 |
| 8 | max min_per_class_accuracy | 0.042025 | 0.700855 | 233.0 |
| 9 | max mean_per_class_accuracy | 0.043801 | 0.715476 | 224.0 |
| 10 | max tns | 0.715010 | 1830.000000 | 0.0 |
| 11 | max fns | 0.715010 | 116.000000 | 0.0 |
| 12 | max fps | 0.000986 | 1830.000000 | 399.0 |
| 13 | max tps | 0.014530 | 117.000000 | 383.0 |
| 14 | max tnr | 0.715010 | 1.000000 | 0.0 |
| 15 | max fnr | 0.715010 | 0.991453 | 0.0 |
| 16 | max fpr | 0.000986 | 1.000000 | 399.0 |
| 17 | max tpr | 0.014530 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.69 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.420300 | 7.488462 | 7.488462 | 0.450000 | 0.498356 | 0.450000 | 0.498356 | 0.076923 | 0.076923 | 648.846154 | 648.846154 | 0.070912 |
| 1 | 2 | 0.020031 | 0.380908 | 7.006748 | 7.253780 | 0.421053 | 0.399743 | 0.435897 | 0.450314 | 0.068376 | 0.145299 | 600.674764 | 625.378041 | 0.133277 |
| 2 | 3 | 0.030303 | 0.355096 | 6.656410 | 7.051282 | 0.400000 | 0.369010 | 0.423729 | 0.422753 | 0.068376 | 0.213675 | 565.641026 | 605.128205 | 0.195096 |
| 3 | 4 | 0.040062 | 0.333994 | 7.882591 | 7.253780 | 0.473684 | 0.342642 | 0.435897 | 0.403239 | 0.076923 | 0.290598 | 688.259109 | 625.378041 | 0.266555 |
| 4 | 5 | 0.050334 | 0.293698 | 4.992308 | 6.792255 | 0.300000 | 0.313827 | 0.408163 | 0.384992 | 0.051282 | 0.341880 | 399.230769 | 579.225536 | 0.310186 |
| 5 | 6 | 0.100154 | 0.063466 | 2.916468 | 4.864300 | 0.175258 | 0.133565 | 0.292308 | 0.259923 | 0.145299 | 0.487179 | 191.646841 | 386.429980 | 0.411770 |
| 6 | 7 | 0.149974 | 0.052045 | 0.343114 | 3.362399 | 0.020619 | 0.056769 | 0.202055 | 0.192437 | 0.017094 | 0.504274 | -65.688607 | 236.239902 | 0.376951 |
| 7 | 8 | 0.200308 | 0.047556 | 1.528257 | 2.901512 | 0.091837 | 0.049600 | 0.174359 | 0.156545 | 0.076923 | 0.581197 | 52.825746 | 190.151216 | 0.405240 |
| 8 | 9 | 0.299949 | 0.042642 | 0.943563 | 2.251098 | 0.056701 | 0.044743 | 0.135274 | 0.119405 | 0.094017 | 0.675214 | -5.643669 | 125.109765 | 0.399257 |
| 9 | 10 | 0.400103 | 0.038573 | 0.768047 | 1.879859 | 0.046154 | 0.040461 | 0.112965 | 0.099644 | 0.076923 | 0.752137 | -23.195266 | 87.985912 | 0.374541 |
| 10 | 11 | 0.500257 | 0.035586 | 0.682709 | 1.640183 | 0.041026 | 0.037041 | 0.098563 | 0.087110 | 0.068376 | 0.820513 | -31.729126 | 64.018323 | 0.340731 |
| 11 | 12 | 0.599897 | 0.032635 | 0.343114 | 1.424745 | 0.020619 | 0.034046 | 0.085616 | 0.078297 | 0.034188 | 0.854701 | -65.688607 | 42.474535 | 0.271094 |
| 12 | 13 | 0.700051 | 0.029692 | 0.512032 | 1.294166 | 0.030769 | 0.031111 | 0.077770 | 0.071546 | 0.051282 | 0.905983 | -48.796844 | 29.416634 | 0.219098 |
| 13 | 14 | 0.799692 | 0.025835 | 0.257335 | 1.164979 | 0.015464 | 0.027722 | 0.070006 | 0.066085 | 0.025641 | 0.931624 | -74.266455 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.021328 | 0.256016 | 1.063810 | 0.015385 | 0.023626 | 0.063927 | 0.061360 | 0.025641 | 0.957265 | -74.398422 | 6.380986 | 0.061090 |
| 15 | 16 | 1.000000 | 0.000870 | 0.426693 | 1.000000 | 0.025641 | 0.016861 | 0.060092 | 0.056903 | 0.042735 | 1.000000 | -57.330703 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04813763172064661 RMSE: 0.21940289815917796 LogLoss: 0.187006249113437 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.0428475257954 Residual deviance: 2912.0613111944417 AIC: 2942.0613111944417 AUC: 0.7620616417077198 AUCPR: 0.28832926933838643 Gini: 0.5241232834154397 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.27696549633438455:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7059.0 | 259.0 | 0.0354 | (259.0/7318.0) |
| 1 | 1 | 279.0 | 189.0 | 0.5962 | (279.0/468.0) |
| 2 | Total | 7338.0 | 448.0 | 0.0691 | (538.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.276965 | 0.412664 | 138.0 |
| 1 | max f2 | 0.077236 | 0.433503 | 218.0 |
| 2 | max f0point5 | 0.340712 | 0.426540 | 101.0 |
| 3 | max accuracy | 0.454597 | 0.940791 | 38.0 |
| 4 | max precision | 0.890557 | 1.000000 | 0.0 |
| 5 | max recall | 0.015909 | 1.000000 | 383.0 |
| 6 | max specificity | 0.890557 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.276965 | 0.376075 | 138.0 |
| 8 | max min_per_class_accuracy | 0.042998 | 0.679487 | 280.0 |
| 9 | max mean_per_class_accuracy | 0.061531 | 0.709502 | 235.0 |
| 10 | max tns | 0.890557 | 7318.000000 | 0.0 |
| 11 | max fns | 0.890557 | 467.000000 | 0.0 |
| 12 | max fps | 0.000949 | 7318.000000 | 399.0 |
| 13 | max tps | 0.015909 | 468.000000 | 383.0 |
| 14 | max tnr | 0.890557 | 1.000000 | 0.0 |
| 15 | max fnr | 0.890557 | 0.997863 | 0.0 |
| 16 | max fpr | 0.000949 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015909 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.435576 | 8.744959 | 8.744959 | 0.525641 | 0.523449 | 0.525641 | 0.523449 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.391501 | 7.678501 | 8.211730 | 0.461538 | 0.412400 | 0.493590 | 0.467924 | 0.076923 | 0.164530 | 667.850099 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.362150 | 7.251918 | 7.891793 | 0.435897 | 0.377700 | 0.474359 | 0.437850 | 0.072650 | 0.237179 | 625.191760 | 689.179268 | 0.220372 |
| 3 | 4 | 0.040072 | 0.337327 | 7.251918 | 7.731824 | 0.435897 | 0.348547 | 0.464744 | 0.415524 | 0.072650 | 0.309829 | 625.191760 | 673.182391 | 0.287009 |
| 4 | 5 | 0.050090 | 0.308138 | 5.758876 | 7.337234 | 0.346154 | 0.323760 | 0.441026 | 0.397171 | 0.057692 | 0.367521 | 475.887574 | 633.723428 | 0.337732 |
| 5 | 6 | 0.100051 | 0.066676 | 2.395008 | 4.869293 | 0.143959 | 0.149940 | 0.292683 | 0.273714 | 0.119658 | 0.487179 | 139.500802 | 386.929331 | 0.411886 |
| 6 | 7 | 0.180452 | 0.060183 | 0.956746 | 3.126052 | 0.057508 | 0.060849 | 0.187900 | 0.178872 | 0.076923 | 0.564103 | -4.325387 | 212.605165 | 0.408186 |
| 7 | 8 | 0.200103 | 0.055419 | 0.652422 | 2.883134 | 0.039216 | 0.057696 | 0.173299 | 0.166972 | 0.012821 | 0.576923 | -34.757835 | 188.313420 | 0.400919 |
| 8 | 9 | 0.300026 | 0.044811 | 0.705672 | 2.157935 | 0.042416 | 0.049063 | 0.129709 | 0.127702 | 0.070513 | 0.647436 | -29.432799 | 115.793489 | 0.369628 |
| 9 | 10 | 0.400077 | 0.040130 | 0.662053 | 1.783844 | 0.039795 | 0.042363 | 0.107223 | 0.106361 | 0.066239 | 0.713675 | -33.794696 | 78.384437 | 0.333653 |
| 10 | 11 | 0.500000 | 0.036550 | 0.641520 | 1.555556 | 0.038560 | 0.038250 | 0.093501 | 0.092749 | 0.064103 | 0.777778 | -35.847999 | 55.555556 | 0.295542 |
| 11 | 12 | 0.600051 | 0.033342 | 0.619340 | 1.399453 | 0.037227 | 0.034959 | 0.084118 | 0.083113 | 0.061966 | 0.839744 | -38.066006 | 39.945282 | 0.255021 |
| 12 | 13 | 0.699974 | 0.030281 | 0.555984 | 1.279046 | 0.033419 | 0.031806 | 0.076881 | 0.075789 | 0.055556 | 0.895299 | -44.401600 | 27.904571 | 0.207816 |
| 13 | 14 | 0.800026 | 0.026754 | 0.448488 | 1.175176 | 0.026958 | 0.028603 | 0.070637 | 0.069888 | 0.044872 | 0.940171 | -55.151246 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.021961 | 0.342144 | 1.082683 | 0.020566 | 0.024594 | 0.065078 | 0.064859 | 0.034188 | 0.974359 | -65.785600 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.000659 | 0.256279 | 1.000000 | 0.015404 | 0.017146 | 0.060108 | 0.060085 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9338506 | 0.020114219 | 0.9307692 | 0.9423077 | 0.9269231 | 0.9423077 | 0.8730769 | 0.9692308 | 0.9346154 | 0.95384616 | 0.91923076 | 0.93846154 | 0.9 | 0.9461538 | 0.95 | 0.96153843 | 0.93846154 | 0.95384616 | 0.9150579 | 0.8918919 | 0.93436295 | 0.94208497 | 0.94208497 | 0.93822396 | 0.9150579 | 0.93050194 | 0.93822396 | 0.95366794 | 0.93436295 | 0.93822396 | 0.9227799 | 0.93822396 |
| 1 | auc | 0.76718664 | 0.069745615 | 0.73438936 | 0.8021176 | 0.77520835 | 0.79094076 | 0.76938194 | 0.6538679 | 0.828495 | 0.80737704 | 0.73784626 | 0.82347083 | 0.68010753 | 0.9018583 | 0.7791058 | 0.8565986 | 0.8327526 | 0.89375687 | 0.71973747 | 0.58743167 | 0.7060117 | 0.75101215 | 0.7140351 | 0.8180328 | 0.8252309 | 0.75899124 | 0.77923495 | 0.6991803 | 0.7604753 | 0.68400586 | 0.73699564 | 0.8079498 |
| 2 | err | 0.06614939 | 0.020114219 | 0.06923077 | 0.057692308 | 0.073076926 | 0.057692308 | 0.12692308 | 0.03076923 | 0.06538462 | 0.046153847 | 0.08076923 | 0.06153846 | 0.1 | 0.053846154 | 0.05 | 0.03846154 | 0.06153846 | 0.046153847 | 0.08494209 | 0.10810811 | 0.06563707 | 0.057915058 | 0.057915058 | 0.06177606 | 0.08494209 | 0.06949807 | 0.06177606 | 0.046332046 | 0.06563707 | 0.06177606 | 0.077220075 | 0.06177606 |
| 3 | err_count | 17.166666 | 5.219878 | 18.0 | 15.0 | 19.0 | 15.0 | 33.0 | 8.0 | 17.0 | 12.0 | 21.0 | 16.0 | 26.0 | 14.0 | 13.0 | 10.0 | 16.0 | 12.0 | 22.0 | 28.0 | 17.0 | 15.0 | 15.0 | 16.0 | 22.0 | 18.0 | 16.0 | 12.0 | 17.0 | 16.0 | 20.0 | 16.0 |
| 4 | f0point5 | 0.45903078 | 0.11957584 | 0.47297296 | 0.42168674 | 0.5208333 | 0.47297296 | 0.32258064 | 0.42857143 | 0.6122449 | 0.625 | 0.5339806 | 0.6179775 | 0.2 | 0.5 | 0.3508772 | 0.6779661 | 0.44871795 | 0.5063291 | 0.37634408 | 0.23364486 | 0.2542373 | 0.390625 | 0.6 | 0.48192772 | 0.3960396 | 0.49295774 | 0.39215687 | 0.5714286 | 0.38961038 | 0.4918033 | 0.38961038 | 0.59782606 |
| 5 | f1 | 0.44951442 | 0.10014942 | 0.4375 | 0.4827586 | 0.51282054 | 0.4827586 | 0.3773585 | 0.42857143 | 0.58536583 | 0.5714286 | 0.5116279 | 0.57894737 | 0.23529412 | 0.5 | 0.3809524 | 0.61538464 | 0.46666667 | 0.5714286 | 0.3888889 | 0.2631579 | 0.26086956 | 0.4 | 0.54545456 | 0.5 | 0.42105263 | 0.4375 | 0.33333334 | 0.4 | 0.41379312 | 0.42857143 | 0.375 | 0.57894737 |
| 6 | f2 | 0.4489144 | 0.09772691 | 0.40697673 | 0.5645161 | 0.5050505 | 0.49295774 | 0.45454547 | 0.42857143 | 0.5607477 | 0.5263158 | 0.49107143 | 0.5445545 | 0.2857143 | 0.5 | 0.41666666 | 0.5633803 | 0.4861111 | 0.6557377 | 0.40229884 | 0.30120483 | 0.26785713 | 0.40983605 | 0.5 | 0.5194805 | 0.4494382 | 0.39325842 | 0.28985506 | 0.30769232 | 0.44117647 | 0.37974682 | 0.36144578 | 0.56122446 |
| 7 | lift_top_group | 7.6274705 | 4.118171 | 0.0 | 7.878788 | 8.666667 | 12.380953 | 4.5614033 | 12.380953 | 7.878788 | 5.4166665 | 3.768116 | 8.253968 | 0.0 | 12.380953 | 9.62963 | 17.333334 | 12.380953 | 7.878788 | 5.0784316 | 5.7555556 | 7.848485 | 7.1944447 | 4.5438595 | 11.511111 | 5.0784316 | 9.087719 | 5.7555556 | 11.511111 | 6.6410255 | 5.0784316 | 0.0 | 12.95 |
| 8 | logloss | 0.18562369 | 0.033936832 | 0.21805398 | 0.14706908 | 0.21907215 | 0.16151807 | 0.22925472 | 0.1181114 | 0.21801753 | 0.17337331 | 0.24925251 | 0.19910976 | 0.18121426 | 0.15210144 | 0.12901443 | 0.15584429 | 0.16367532 | 0.12028217 | 0.21102196 | 0.21442321 | 0.16481145 | 0.16111712 | 0.213916 | 0.1665701 | 0.1980989 | 0.22017941 | 0.19624183 | 0.19158995 | 0.1714461 | 0.2138312 | 0.21842793 | 0.192071 |
| 9 | max_per_class_error | 0.54675597 | 0.10637419 | 0.6111111 | 0.36363637 | 0.5 | 0.5 | 0.47368422 | 0.5714286 | 0.45454547 | 0.5 | 0.5217391 | 0.47619048 | 0.6666667 | 0.5 | 0.5555556 | 0.46666667 | 0.5 | 0.27272728 | 0.5882353 | 0.6666667 | 0.72727275 | 0.5833333 | 0.5263158 | 0.46666667 | 0.5294118 | 0.6315789 | 0.73333335 | 0.73333335 | 0.53846157 | 0.64705884 | 0.64705884 | 0.45 |
| 10 | mcc | 0.42199793 | 0.10498857 | 0.404828 | 0.46958143 | 0.4735295 | 0.452549 | 0.32941666 | 0.41276115 | 0.5518235 | 0.55389905 | 0.46919408 | 0.54969865 | 0.19658276 | 0.4715447 | 0.35945237 | 0.6035929 | 0.43525764 | 0.5627649 | 0.3440105 | 0.2131391 | 0.22682892 | 0.36995202 | 0.5221443 | 0.4682548 | 0.37821072 | 0.41008145 | 0.3139735 | 0.44573936 | 0.38171807 | 0.40805075 | 0.33476225 | 0.54659563 |
| 11 | mean_per_class_accuracy | 0.708853 | 0.053447824 | 0.67998165 | 0.79609346 | 0.73125 | 0.73373985 | 0.7133654 | 0.7063806 | 0.7580214 | 0.7418033 | 0.7201431 | 0.74935246 | 0.63037634 | 0.7357724 | 0.70628595 | 0.76054424 | 0.73170733 | 0.84556407 | 0.681089 | 0.6297814 | 0.6182185 | 0.692139 | 0.7264254 | 0.748224 | 0.70843464 | 0.67171055 | 0.6230874 | 0.6312842 | 0.71044403 | 0.66614 | 0.65787554 | 0.76035565 |
| 12 | mean_per_class_error | 0.29114696 | 0.053447824 | 0.32001835 | 0.20390654 | 0.26875 | 0.26626018 | 0.28663462 | 0.29361942 | 0.24197862 | 0.2581967 | 0.27985692 | 0.25064754 | 0.36962366 | 0.26422763 | 0.29371405 | 0.23945579 | 0.2682927 | 0.15443593 | 0.31891105 | 0.37021858 | 0.38178152 | 0.307861 | 0.27357456 | 0.25177595 | 0.2915654 | 0.32828948 | 0.37691256 | 0.36871585 | 0.28955597 | 0.33385998 | 0.34212446 | 0.23964435 |
| 13 | mse | 0.04783004 | 0.010247805 | 0.057385042 | 0.035958126 | 0.058631968 | 0.040848304 | 0.061598573 | 0.025608491 | 0.060672574 | 0.04534935 | 0.06720534 | 0.052870266 | 0.043656904 | 0.039778262 | 0.030244768 | 0.04023881 | 0.042023182 | 0.03023903 | 0.054760415 | 0.05371934 | 0.039606217 | 0.03994141 | 0.05510662 | 0.043711413 | 0.053071283 | 0.05798326 | 0.050765574 | 0.048160855 | 0.043021422 | 0.055135787 | 0.05771895 | 0.049889717 |
| 14 | null_deviance | 118.03476 | 21.291328 | 131.24835 | 92.82863 | 142.31174 | 109.23703 | 136.7752 | 71.082695 | 153.41394 | 120.223526 | 158.97968 | 147.85797 | 98.28862 | 109.23703 | 81.936905 | 114.7255 | 109.23703 | 92.82863 | 125.607956 | 114.60118 | 92.701996 | 98.16257 | 136.65318 | 114.60118 | 125.607956 | 136.65318 | 114.60118 | 114.60118 | 103.63261 | 125.607956 | 125.607956 | 142.19029 |
| 15 | pr_auc | 0.31115896 | 0.12307921 | 0.24175657 | 0.30357695 | 0.32888365 | 0.40735927 | 0.28395408 | 0.1779738 | 0.44018152 | 0.37689754 | 0.3595513 | 0.5408352 | 0.094048984 | 0.39154574 | 0.27040443 | 0.57678413 | 0.35912415 | 0.36897686 | 0.25270233 | 0.11329785 | 0.12004446 | 0.18642889 | 0.34615996 | 0.38093603 | 0.2803875 | 0.35132715 | 0.18974859 | 0.34918052 | 0.19259602 | 0.2831585 | 0.2190429 | 0.5479038 |
| 16 | precision | 0.4726558 | 0.14819708 | 0.5 | 0.3888889 | 0.5263158 | 0.46666667 | 0.29411766 | 0.42857143 | 0.6315789 | 0.6666667 | 0.55 | 0.64705884 | 0.18181819 | 0.5 | 0.33333334 | 0.72727275 | 0.4375 | 0.47058824 | 0.36842105 | 0.2173913 | 0.25 | 0.3846154 | 0.64285713 | 0.47058824 | 0.3809524 | 0.53846157 | 0.44444445 | 0.8 | 0.375 | 0.54545456 | 0.4 | 0.6111111 |
| 17 | r2 | 0.14211036 | 0.080727436 | 0.109451585 | 0.11253403 | 0.17426644 | 0.19821568 | 0.09061725 | 0.022510462 | 0.21667951 | 0.21475002 | 0.16656007 | 0.28789997 | 0.008331083 | 0.21921879 | 0.09493301 | 0.2598249 | 0.17515473 | 0.25368437 | 0.10710174 | 0.015424253 | 0.02609068 | 0.09604934 | 0.18934053 | 0.19885099 | 0.13464397 | 0.147023 | 0.069561385 | 0.11730102 | 0.09758602 | 0.100981094 | 0.05886123 | 0.29986355 |
| 18 | recall | 0.45324406 | 0.10637419 | 0.3888889 | 0.6363636 | 0.5 | 0.5 | 0.5263158 | 0.42857143 | 0.54545456 | 0.5 | 0.47826087 | 0.52380955 | 0.33333334 | 0.5 | 0.44444445 | 0.53333336 | 0.5 | 0.72727275 | 0.4117647 | 0.33333334 | 0.27272728 | 0.41666666 | 0.47368422 | 0.53333336 | 0.47058824 | 0.36842105 | 0.26666668 | 0.26666668 | 0.46153846 | 0.3529412 | 0.3529412 | 0.55 |
| 19 | residual_deviance | 96.342064 | 17.582438 | 113.38807 | 76.47592 | 113.91752 | 83.989395 | 119.212456 | 61.41793 | 113.36912 | 90.15412 | 129.6113 | 103.53708 | 94.231415 | 79.09274 | 67.0875 | 81.039024 | 85.11117 | 62.54673 | 109.30937 | 111.07122 | 85.37233 | 83.45867 | 110.80849 | 86.28331 | 102.61523 | 114.05293 | 101.65327 | 99.24359 | 88.809074 | 110.764565 | 113.14567 | 99.49278 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:14:40 | 0.000 sec | 2 | .85E1 | 15 | 0.452090 | 0.452100 | 0.452480 | 0.014857 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:14:40 | 0.004 sec | 4 | .53E1 | 15 | 0.450638 | 0.450703 | 0.451093 | 0.014788 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:14:40 | 0.007 sec | 6 | .33E1 | 15 | 0.448357 | 0.448507 | 0.448912 | 0.014681 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:14:40 | 0.011 sec | 8 | .2E1 | 15 | 0.444813 | 0.445091 | 0.445519 | 0.014515 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:14:40 | 0.014 sec | 10 | .13E1 | 15 | 0.439477 | 0.439937 | 0.440399 | 0.014268 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:14:40 | 0.017 sec | 12 | .78E0 | 15 | 0.431796 | 0.432500 | 0.432999 | 0.013922 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:14:40 | 0.024 sec | 14 | .49E0 | 15 | 0.421578 | 0.422558 | 0.423089 | 0.013481 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:14:40 | 0.028 sec | 16 | .3E0 | 15 | 0.409637 | 0.410864 | 0.411395 | 0.013000 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:14:40 | 0.031 sec | 18 | .19E0 | 15 | 0.397917 | 0.399291 | 0.399798 | 0.012586 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:14:40 | 0.035 sec | 20 | .12E0 | 15 | 0.388364 | 0.389802 | 0.390302 | 0.012321 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:14:40 | 0.039 sec | 22 | .72E-1 | 15 | 0.381648 | 0.383175 | 0.383664 | 0.012202 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:14:40 | 0.042 sec | 24 | .45E-1 | 15 | 0.377330 | 0.379063 | 0.379493 | 0.012179 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:14:40 | 0.046 sec | 26 | .28E-1 | 15 | 0.374661 | 0.376737 | 0.377026 | 0.012201 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:14:40 | 0.050 sec | 28 | .17E-1 | 15 | 0.373020 | 0.375530 | 0.375609 | 0.012237 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:14:40 | 0.053 sec | 30 | .11E-1 | 15 | 0.372012 | 0.374985 | 0.374832 | 0.012303 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:14:40 | 0.056 sec | 32 | .67E-2 | 15 | 0.371403 | 0.374812 | 0.374313 | 0.012332 | 0.0 | 32.0 | 0.219057 | 0.185524 | 0.150618 | 0.772731 | 0.293174 | 8.744959 | 0.067172 | 0.220123 | 0.187417 | 0.142121 | 0.768621 | 0.272074 | 7.488462 | 0.072933 | |
| 16 | 2021-07-15 20:14:40 | 0.060 sec | 34 | .41E-2 | 15 | 0.371048 | 0.374834 | 0.374047 | 0.012359 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:14:40 | 0.063 sec | 36 | .26E-2 | 15 | 0.370850 | 0.374937 | 0.380109 | 0.013310 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:14:40 | 0.065 sec | 37 | .16E-2 | 15 | 0.370744 | 0.375054 | 0.383708 | 0.013440 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:14:40 | 0.067 sec | 38 | .99E-3 | 15 | 0.370682 | 0.375157 | 0.386712 | 0.013212 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.574477 | 1.000000 | 0.237504 |
| 1 | Average_Transaction_Frequency | 0.283708 | 0.493855 | 0.117292 |
| 2 | Card_Type.1 | 0.238616 | 0.415361 | 0.098650 |
| 3 | Card_Type.0 | 0.234221 | 0.407712 | 0.096833 |
| 4 | Merchant_ID | 0.228494 | 0.397742 | 0.094465 |
| 5 | Minimum_Transaction_Amount | 0.197092 | 0.343080 | 0.081483 |
| 6 | Channel_ID | 0.162490 | 0.282849 | 0.067178 |
| 7 | Transaction_Amount | 0.129546 | 0.225502 | 0.053558 |
| 8 | Transaction_Date | 0.102434 | 0.178309 | 0.042349 |
| 9 | Maximum_Transaction_Amount | 0.082257 | 0.143186 | 0.034007 |
| 10 | Average_Transaction_Amount | 0.066616 | 0.115959 | 0.027541 |
| 11 | Day | 0.060157 | 0.104716 | 0.024870 |
| 12 | Month | 0.032255 | 0.056147 | 0.013335 |
| 13 | City_ID | 0.026452 | 0.046046 | 0.010936 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201443 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01129 ) | nlambda = 30, lambda.max = 8.8938, lambda.min = 0.01129, lambda.1s... | 14 | 14 | 30 | automl_training_py_601_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04747696486696619 RMSE: 0.21789209454903632 LogLoss: 0.18321276591067698 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793842 Residual deviance: 2852.9891907610613 AIC: 2882.9891907610613 AUC: 0.7853286767436809 AUCPR: 0.29895288272561715 Gini: 0.5706573534873618 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2732666052738136:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7024.0 | 294.0 | 0.0402 | (294.0/7318.0) |
| 1 | 1 | 261.0 | 207.0 | 0.5577 | (261.0/468.0) |
| 2 | Total | 7285.0 | 501.0 | 0.0713 | (555.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.273267 | 0.427245 | 145.0 |
| 1 | max f2 | 0.075754 | 0.455785 | 217.0 |
| 2 | max f0point5 | 0.324403 | 0.432510 | 118.0 |
| 3 | max accuracy | 0.562966 | 0.940406 | 9.0 |
| 4 | max precision | 0.872708 | 1.000000 | 0.0 |
| 5 | max recall | 0.018694 | 1.000000 | 380.0 |
| 6 | max specificity | 0.872708 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.273267 | 0.389543 | 145.0 |
| 8 | max min_per_class_accuracy | 0.041089 | 0.707265 | 289.0 |
| 9 | max mean_per_class_accuracy | 0.055565 | 0.724652 | 244.0 |
| 10 | max tns | 0.872708 | 7318.000000 | 0.0 |
| 11 | max fns | 0.872708 | 467.000000 | 0.0 |
| 12 | max fps | 0.001487 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018694 | 468.000000 | 380.0 |
| 14 | max tnr | 0.872708 | 1.000000 | 0.0 |
| 15 | max fnr | 0.872708 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001487 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018694 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.428300 | 8.318376 | 8.318376 | 0.500000 | 0.502061 | 0.500000 | 0.502061 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.395288 | 7.251918 | 7.785147 | 0.435897 | 0.412204 | 0.467949 | 0.457133 | 0.072650 | 0.155983 | 625.191760 | 678.514683 | 0.144641 |
| 2 | 3 | 0.030054 | 0.369493 | 8.105084 | 7.891793 | 0.487179 | 0.382435 | 0.474359 | 0.432233 | 0.081197 | 0.237179 | 710.508437 | 689.179268 | 0.220372 |
| 3 | 4 | 0.040072 | 0.349634 | 6.612043 | 7.571855 | 0.397436 | 0.359512 | 0.455128 | 0.414053 | 0.066239 | 0.303419 | 561.204252 | 657.185514 | 0.280188 |
| 4 | 5 | 0.050090 | 0.329689 | 6.398751 | 7.337234 | 0.384615 | 0.341187 | 0.441026 | 0.399480 | 0.064103 | 0.367521 | 539.875082 | 633.723428 | 0.337732 |
| 5 | 6 | 0.100051 | 0.066098 | 2.865456 | 5.104215 | 0.172237 | 0.171752 | 0.306804 | 0.285762 | 0.143162 | 0.510684 | 186.545602 | 410.421535 | 0.436893 |
| 6 | 7 | 0.150013 | 0.051144 | 0.898128 | 3.703387 | 0.053985 | 0.056496 | 0.222603 | 0.209405 | 0.044872 | 0.555556 | -10.187199 | 270.338661 | 0.431478 |
| 7 | 8 | 0.200103 | 0.046396 | 1.322409 | 3.107378 | 0.079487 | 0.048498 | 0.186778 | 0.169127 | 0.066239 | 0.621795 | 32.240850 | 210.737797 | 0.448660 |
| 8 | 9 | 0.300026 | 0.041620 | 0.769824 | 2.328860 | 0.046272 | 0.043765 | 0.139983 | 0.127375 | 0.076923 | 0.698718 | -23.017599 | 132.886042 | 0.424189 |
| 9 | 10 | 0.400077 | 0.038397 | 0.576627 | 1.890661 | 0.034660 | 0.039923 | 0.113644 | 0.105505 | 0.057692 | 0.756410 | -42.337316 | 89.066140 | 0.379121 |
| 10 | 11 | 0.500000 | 0.035875 | 0.577368 | 1.628205 | 0.034704 | 0.037129 | 0.097868 | 0.091841 | 0.057692 | 0.814103 | -42.263200 | 62.820513 | 0.334190 |
| 11 | 12 | 0.600051 | 0.033520 | 0.576627 | 1.452867 | 0.034660 | 0.034685 | 0.087329 | 0.082311 | 0.057692 | 0.871795 | -42.337316 | 45.286705 | 0.289122 |
| 12 | 13 | 0.699974 | 0.031017 | 0.299376 | 1.288204 | 0.017995 | 0.032263 | 0.077431 | 0.075166 | 0.029915 | 0.901709 | -70.062400 | 28.820356 | 0.214636 |
| 13 | 14 | 0.800026 | 0.028147 | 0.469844 | 1.185859 | 0.028241 | 0.029660 | 0.071279 | 0.069475 | 0.047009 | 0.948718 | -53.015591 | 18.585936 | 0.158201 |
| 14 | 15 | 0.899949 | 0.023876 | 0.342144 | 1.092180 | 0.020566 | 0.026139 | 0.065649 | 0.064663 | 0.034188 | 0.982906 | -65.785600 | 9.218010 | 0.088263 |
| 15 | 16 | 1.000000 | 0.001222 | 0.170852 | 1.000000 | 0.010270 | 0.019132 | 0.060108 | 0.060108 | 0.017094 | 1.000000 | -82.914760 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.05059879555225068 RMSE: 0.22494176035643243 LogLoss: 0.19811668385325432 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311424 Residual deviance: 771.4663669245724 AIC: 801.4663669245724 AUC: 0.7184414553267012 AUCPR: 0.25162367779510086 Gini: 0.43688291065340246 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.10628714696250904:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1736.0 | 94.0 | 0.0514 | (94.0/1830.0) |
| 1 | 1 | 72.0 | 45.0 | 0.6154 | (72.0/117.0) |
| 2 | Total | 1808.0 | 139.0 | 0.0853 | (166.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.106287 | 0.351563 | 113.0 |
| 1 | max f2 | 0.055043 | 0.383523 | 166.0 |
| 2 | max f0point5 | 0.355371 | 0.399002 | 59.0 |
| 3 | max accuracy | 0.452702 | 0.941962 | 15.0 |
| 4 | max precision | 0.548316 | 0.750000 | 3.0 |
| 5 | max recall | 0.018035 | 1.000000 | 379.0 |
| 6 | max specificity | 0.844743 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.352420 | 0.319965 | 62.0 |
| 8 | max min_per_class_accuracy | 0.038990 | 0.641026 | 244.0 |
| 9 | max mean_per_class_accuracy | 0.055043 | 0.681042 | 166.0 |
| 10 | max tns | 0.844743 | 1829.000000 | 0.0 |
| 11 | max fns | 0.844743 | 117.000000 | 0.0 |
| 12 | max fps | 0.001658 | 1830.000000 | 399.0 |
| 13 | max tps | 0.018035 | 117.000000 | 379.0 |
| 14 | max tnr | 0.844743 | 0.999454 | 0.0 |
| 15 | max fnr | 0.844743 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001658 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018035 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.88 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.426293 | 9.984615 | 9.984615 | 0.600000 | 0.531004 | 0.600000 | 0.531004 | 0.102564 | 0.102564 | 898.461538 | 898.461538 | 0.098193 |
| 1 | 2 | 0.020031 | 0.390473 | 7.006748 | 8.533859 | 0.421053 | 0.402940 | 0.512821 | 0.468614 | 0.068376 | 0.170940 | 600.674764 | 753.385930 | 0.160558 |
| 2 | 3 | 0.030303 | 0.367171 | 5.824359 | 7.615385 | 0.350000 | 0.376861 | 0.457627 | 0.437511 | 0.059829 | 0.230769 | 482.435897 | 661.538462 | 0.213283 |
| 3 | 4 | 0.040062 | 0.349433 | 5.255061 | 7.040434 | 0.315789 | 0.358256 | 0.423077 | 0.418205 | 0.051282 | 0.282051 | 425.506073 | 604.043393 | 0.257461 |
| 4 | 5 | 0.050334 | 0.325472 | 3.328205 | 6.282836 | 0.200000 | 0.337588 | 0.377551 | 0.401753 | 0.034188 | 0.316239 | 232.820513 | 528.283621 | 0.282906 |
| 5 | 6 | 0.100154 | 0.059621 | 2.230241 | 4.266930 | 0.134021 | 0.151289 | 0.256410 | 0.277163 | 0.111111 | 0.427350 | 123.024055 | 326.692965 | 0.348115 |
| 6 | 7 | 0.149974 | 0.050036 | 0.686228 | 3.077450 | 0.041237 | 0.054301 | 0.184932 | 0.203130 | 0.034188 | 0.461538 | -31.377214 | 207.744995 | 0.331484 |
| 7 | 8 | 0.200308 | 0.045434 | 0.849032 | 2.517488 | 0.051020 | 0.047578 | 0.151282 | 0.164043 | 0.042735 | 0.504274 | -15.096808 | 151.748849 | 0.323399 |
| 8 | 9 | 0.299949 | 0.040830 | 0.772006 | 1.937654 | 0.046392 | 0.042874 | 0.116438 | 0.123791 | 0.076923 | 0.581197 | -22.799366 | 93.765367 | 0.299229 |
| 9 | 10 | 0.400103 | 0.038116 | 0.682709 | 1.623515 | 0.041026 | 0.039430 | 0.097561 | 0.102674 | 0.068376 | 0.649573 | -31.729126 | 62.351470 | 0.265420 |
| 10 | 11 | 0.500257 | 0.035243 | 0.938725 | 1.486416 | 0.056410 | 0.036661 | 0.089322 | 0.089458 | 0.094017 | 0.743590 | -6.127548 | 48.641605 | 0.258890 |
| 11 | 12 | 0.599897 | 0.032857 | 0.686228 | 1.353508 | 0.041237 | 0.033974 | 0.081336 | 0.080242 | 0.068376 | 0.811966 | -31.377214 | 35.350808 | 0.225627 |
| 12 | 13 | 0.700051 | 0.030685 | 0.682709 | 1.257539 | 0.041026 | 0.031731 | 0.075569 | 0.073302 | 0.068376 | 0.880342 | -31.729126 | 25.753899 | 0.191817 |
| 13 | 14 | 0.799692 | 0.027539 | 0.343114 | 1.143603 | 0.020619 | 0.029255 | 0.068722 | 0.067814 | 0.034188 | 0.914530 | -65.688607 | 14.360292 | 0.122180 |
| 14 | 15 | 0.899846 | 0.023967 | 0.512032 | 1.073308 | 0.030769 | 0.025914 | 0.064498 | 0.063150 | 0.051282 | 0.965812 | -48.796844 | 7.330816 | 0.070184 |
| 15 | 16 | 1.000000 | 0.001535 | 0.341354 | 1.000000 | 0.020513 | 0.019479 | 0.060092 | 0.058776 | 0.034188 | 1.000000 | -65.864563 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.047644493452616156 RMSE: 0.21827618617846556 LogLoss: 0.18416844581653768 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.212976631786 Residual deviance: 2867.871038255125 AIC: 2897.871038255125 AUC: 0.7771278173710532 AUCPR: 0.28772430561910384 Gini: 0.5542556347421064 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.26495242742411435:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7033.0 | 285.0 | 0.0389 | (285.0/7318.0) |
| 1 | 1 | 262.0 | 206.0 | 0.5598 | (262.0/468.0) |
| 2 | Total | 7295.0 | 491.0 | 0.0703 | (547.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.264952 | 0.429614 | 145.0 |
| 1 | max f2 | 0.095426 | 0.453263 | 200.0 |
| 2 | max f0point5 | 0.315388 | 0.426829 | 120.0 |
| 3 | max accuracy | 0.580074 | 0.940149 | 9.0 |
| 4 | max precision | 0.663574 | 0.600000 | 4.0 |
| 5 | max recall | 0.018375 | 1.000000 | 380.0 |
| 6 | max specificity | 0.872736 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.264952 | 0.392333 | 145.0 |
| 8 | max min_per_class_accuracy | 0.041795 | 0.702924 | 283.0 |
| 9 | max mean_per_class_accuracy | 0.061311 | 0.718645 | 233.0 |
| 10 | max tns | 0.872736 | 7317.000000 | 0.0 |
| 11 | max fns | 0.872736 | 468.000000 | 0.0 |
| 12 | max fps | 0.001573 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018375 | 468.000000 | 380.0 |
| 14 | max tnr | 0.872736 | 0.999863 | 0.0 |
| 15 | max fnr | 0.872736 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001573 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018375 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.03 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.427171 | 7.678501 | 7.678501 | 0.461538 | 0.503675 | 0.461538 | 0.503675 | 0.076923 | 0.076923 | 667.850099 | 667.850099 | 0.071184 |
| 1 | 2 | 0.020036 | 0.390908 | 7.038626 | 7.358563 | 0.423077 | 0.408072 | 0.442308 | 0.455873 | 0.070513 | 0.147436 | 603.862590 | 635.856345 | 0.135547 |
| 2 | 3 | 0.030054 | 0.367207 | 7.891793 | 7.536307 | 0.474359 | 0.379364 | 0.452991 | 0.430370 | 0.079060 | 0.226496 | 689.179268 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.346262 | 7.251918 | 7.465209 | 0.435897 | 0.355775 | 0.448718 | 0.411721 | 0.072650 | 0.299145 | 625.191760 | 646.520929 | 0.275642 |
| 4 | 5 | 0.050090 | 0.323815 | 6.825334 | 7.337234 | 0.410256 | 0.336945 | 0.441026 | 0.396766 | 0.068376 | 0.367521 | 582.533421 | 633.723428 | 0.337732 |
| 5 | 6 | 0.100051 | 0.064294 | 2.779920 | 5.061502 | 0.167095 | 0.161538 | 0.304236 | 0.279303 | 0.138889 | 0.506410 | 177.992002 | 406.150225 | 0.432346 |
| 6 | 7 | 0.150013 | 0.056838 | 0.898128 | 3.674899 | 0.053985 | 0.060545 | 0.220890 | 0.206446 | 0.044872 | 0.551282 | -10.187199 | 267.489902 | 0.426931 |
| 7 | 8 | 0.200103 | 0.048613 | 0.853167 | 2.968560 | 0.051282 | 0.052055 | 0.178434 | 0.167799 | 0.042735 | 0.594017 | -14.683322 | 196.856039 | 0.419106 |
| 8 | 9 | 0.300026 | 0.042570 | 0.898128 | 2.279007 | 0.053985 | 0.045166 | 0.136986 | 0.126956 | 0.089744 | 0.683761 | -10.187199 | 127.900714 | 0.408276 |
| 9 | 10 | 0.400077 | 0.039038 | 0.640696 | 1.869298 | 0.038511 | 0.040679 | 0.112360 | 0.105380 | 0.064103 | 0.747863 | -35.930351 | 86.929799 | 0.370028 |
| 10 | 11 | 0.500000 | 0.036385 | 0.598752 | 1.615385 | 0.035990 | 0.037685 | 0.097097 | 0.091852 | 0.059829 | 0.807692 | -40.124800 | 61.538462 | 0.327370 |
| 11 | 12 | 0.600051 | 0.033898 | 0.533914 | 1.435062 | 0.032092 | 0.035113 | 0.086259 | 0.082391 | 0.053419 | 0.861111 | -46.608626 | 43.506231 | 0.277755 |
| 12 | 13 | 0.699974 | 0.031419 | 0.406296 | 1.288204 | 0.024422 | 0.032661 | 0.077431 | 0.075292 | 0.040598 | 0.901709 | -59.370400 | 28.820356 | 0.214636 |
| 13 | 14 | 0.800026 | 0.028524 | 0.384418 | 1.175176 | 0.023107 | 0.030011 | 0.070637 | 0.069629 | 0.038462 | 0.940171 | -61.558211 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.024219 | 0.384912 | 1.087431 | 0.023136 | 0.026551 | 0.065363 | 0.064846 | 0.038462 | 0.978632 | -61.508800 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000831 | 0.213565 | 1.000000 | 0.012837 | 0.019451 | 0.060108 | 0.060304 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93321997 | 0.021854108 | 0.9269231 | 0.88461536 | 0.9153846 | 0.93846154 | 0.89615387 | 0.9461538 | 0.9307692 | 0.95 | 0.97307694 | 0.95 | 0.91923076 | 0.93846154 | 0.9423077 | 0.9269231 | 0.9115385 | 0.9307692 | 0.98455596 | 0.95366794 | 0.9498069 | 0.9498069 | 0.9150579 | 0.9459459 | 0.9459459 | 0.9189189 | 0.93436295 | 0.94208497 | 0.8918919 | 0.93436295 | 0.93436295 | 0.9150579 |
| 1 | auc | 0.7761352 | 0.08038211 | 0.8057199 | 0.71522164 | 0.7591382 | 0.79134226 | 0.7742364 | 0.8329702 | 0.798646 | 0.8000484 | 0.8462943 | 0.63238096 | 0.8027112 | 0.8608871 | 0.8918733 | 0.7594558 | 0.54728 | 0.75947744 | 0.64426875 | 0.69129556 | 0.86715484 | 0.813388 | 0.7879781 | 0.8300924 | 0.92870545 | 0.81074226 | 0.71775955 | 0.67192084 | 0.77210885 | 0.78600824 | 0.8194444 | 0.7655062 |
| 2 | err | 0.06678002 | 0.021854108 | 0.073076926 | 0.115384616 | 0.08461539 | 0.06153846 | 0.103846155 | 0.053846154 | 0.06923077 | 0.05 | 0.026923077 | 0.05 | 0.08076923 | 0.06153846 | 0.057692308 | 0.073076926 | 0.08846154 | 0.06923077 | 0.015444015 | 0.046332046 | 0.05019305 | 0.05019305 | 0.08494209 | 0.054054055 | 0.054054055 | 0.08108108 | 0.06563707 | 0.057915058 | 0.10810811 | 0.06563707 | 0.06563707 | 0.08494209 |
| 3 | err_count | 17.333334 | 5.677137 | 19.0 | 30.0 | 22.0 | 16.0 | 27.0 | 14.0 | 18.0 | 13.0 | 7.0 | 13.0 | 21.0 | 16.0 | 15.0 | 19.0 | 23.0 | 18.0 | 4.0 | 12.0 | 13.0 | 13.0 | 22.0 | 14.0 | 14.0 | 21.0 | 17.0 | 15.0 | 28.0 | 17.0 | 17.0 | 22.0 |
| 4 | f0point5 | 0.47041526 | 0.1364281 | 0.6372549 | 0.31496063 | 0.37634408 | 0.43209878 | 0.23255815 | 0.5797101 | 0.51724136 | 0.61538464 | 0.71428573 | 0.5319149 | 0.41237113 | 0.41666666 | 0.5882353 | 0.40229884 | 0.13333334 | 0.46875 | 0.6818182 | 0.45454547 | 0.6770833 | 0.54545456 | 0.39130434 | 0.5882353 | 0.505618 | 0.47619048 | 0.3968254 | 0.23255815 | 0.3875969 | 0.47619048 | 0.47619048 | 0.4494382 |
| 5 | f1 | 0.46509498 | 0.11524555 | 0.5777778 | 0.3478261 | 0.3888889 | 0.46666667 | 0.30769232 | 0.53333336 | 0.5 | 0.55172414 | 0.5882353 | 0.4347826 | 0.43243244 | 0.46666667 | 0.61538464 | 0.42424244 | 0.14814815 | 0.5 | 0.6 | 0.4 | 0.6666667 | 0.48 | 0.45 | 0.5882353 | 0.5625 | 0.53333336 | 0.37037036 | 0.21052632 | 0.41666666 | 0.4848485 | 0.4848485 | 0.42105263 |
| 6 | f2 | 0.47055095 | 0.11390654 | 0.52845526 | 0.3883495 | 0.40229884 | 0.5072464 | 0.45454547 | 0.49382716 | 0.48387095 | 0.5 | 0.5 | 0.36764705 | 0.45454547 | 0.530303 | 0.6451613 | 0.44871795 | 0.16666667 | 0.53571427 | 0.53571427 | 0.35714287 | 0.65656567 | 0.42857143 | 0.5294118 | 0.5882353 | 0.63380283 | 0.6060606 | 0.3472222 | 0.1923077 | 0.45045045 | 0.49382716 | 0.49382716 | 0.3960396 |
| 7 | lift_top_group | 8.817806 | 6.1314425 | 6.6666665 | 4.5614033 | 10.196078 | 6.6666665 | 9.62963 | 15.294118 | 9.122807 | 5.098039 | 15.757576 | 11.555555 | 10.196078 | 14.444445 | 9.62963 | 0.0 | 0.0 | 5.4166665 | 28.777779 | 14.388889 | 12.95 | 17.266666 | 5.7555556 | 10.156863 | 6.6410255 | 9.592592 | 5.7555556 | 0.0 | 4.111111 | 0.0 | 10.791667 | 4.111111 |
| 8 | logloss | 0.18208176 | 0.033596028 | 0.24658632 | 0.23823611 | 0.21071726 | 0.16455394 | 0.13733394 | 0.17618938 | 0.21129464 | 0.18577889 | 0.13393575 | 0.19018784 | 0.20150319 | 0.14844193 | 0.16601706 | 0.18432218 | 0.17716119 | 0.18405741 | 0.10259738 | 0.16795062 | 0.17587134 | 0.17019217 | 0.1726119 | 0.17520194 | 0.13257132 | 0.19234988 | 0.19286136 | 0.17158265 | 0.23820503 | 0.18658982 | 0.1790895 | 0.24846065 |
| 9 | max_per_class_error | 0.5174968 | 0.13061945 | 0.5 | 0.57894737 | 0.5882353 | 0.46153846 | 0.33333334 | 0.5294118 | 0.5263158 | 0.5294118 | 0.54545456 | 0.6666667 | 0.5294118 | 0.41666666 | 0.33333334 | 0.53333336 | 0.8181818 | 0.4375 | 0.5 | 0.6666667 | 0.35 | 0.6 | 0.4 | 0.4117647 | 0.30769232 | 0.33333334 | 0.6666667 | 0.8181818 | 0.52380955 | 0.5 | 0.5 | 0.61904764 |
| 10 | mcc | 0.4388528 | 0.120756194 | 0.54678476 | 0.29196033 | 0.34419844 | 0.43903658 | 0.32674357 | 0.5104247 | 0.46375966 | 0.5350431 | 0.6039857 | 0.43351895 | 0.39075184 | 0.44549617 | 0.5864283 | 0.38739952 | 0.10519801 | 0.46663344 | 0.6051444 | 0.385307 | 0.6397921 | 0.46507916 | 0.42271763 | 0.55930966 | 0.5457278 | 0.5029931 | 0.33852848 | 0.18372338 | 0.36155292 | 0.45006707 | 0.45006707 | 0.37821072 |
| 11 | mean_per_class_accuracy | 0.7221045 | 0.0631688 | 0.73717946 | 0.67110723 | 0.68119097 | 0.74898785 | 0.78552455 | 0.72500604 | 0.7202446 | 0.72706366 | 0.72526467 | 0.6605442 | 0.71060276 | 0.7694892 | 0.8147383 | 0.71088433 | 0.56279665 | 0.758709 | 0.7480237 | 0.6585695 | 0.8124477 | 0.6918033 | 0.7672131 | 0.77965486 | 0.8258287 | 0.802213 | 0.6523224 | 0.5788123 | 0.70238096 | 0.7314815 | 0.7314815 | 0.67156863 |
| 12 | mean_per_class_error | 0.2778955 | 0.0631688 | 0.2628205 | 0.32889277 | 0.318809 | 0.25101215 | 0.21447544 | 0.27499396 | 0.2797554 | 0.27293634 | 0.2747353 | 0.33945578 | 0.28939724 | 0.23051076 | 0.18526171 | 0.28911564 | 0.43720335 | 0.24129099 | 0.25197628 | 0.3414305 | 0.1875523 | 0.30819672 | 0.23278688 | 0.22034517 | 0.17417136 | 0.197787 | 0.3476776 | 0.42118767 | 0.29761904 | 0.2685185 | 0.2685185 | 0.32843137 |
| 13 | mse | 0.04700229 | 0.010388563 | 0.06860362 | 0.0636778 | 0.055311296 | 0.04201272 | 0.033595853 | 0.04536412 | 0.055626914 | 0.047978822 | 0.03224481 | 0.0469757 | 0.054248117 | 0.03845261 | 0.044720028 | 0.047642704 | 0.040820047 | 0.048213225 | 0.02056475 | 0.040636677 | 0.047203157 | 0.042880088 | 0.045028828 | 0.046342466 | 0.03507385 | 0.050820958 | 0.04849567 | 0.04041519 | 0.06422501 | 0.04949308 | 0.04694245 | 0.06645814 |
| 14 | null_deviance | 118.040436 | 22.173195 | 175.73576 | 136.7752 | 125.73113 | 103.75808 | 81.936905 | 125.73113 | 136.7752 | 125.73113 | 92.82863 | 114.7255 | 125.73113 | 98.28862 | 131.24835 | 114.7255 | 92.82863 | 120.223526 | 65.54008 | 98.16257 | 142.19029 | 114.60118 | 114.60118 | 125.607956 | 103.63261 | 131.12575 | 114.60118 | 92.701996 | 147.73709 | 120.09978 | 120.09978 | 147.73709 |
| 15 | pr_auc | 0.3243356 | 0.12699975 | 0.48335722 | 0.19957383 | 0.259645 | 0.24329099 | 0.22960837 | 0.4705198 | 0.36505163 | 0.3534665 | 0.48703805 | 0.28599426 | 0.27167445 | 0.2596404 | 0.48825163 | 0.20166758 | 0.054562937 | 0.2650821 | 0.46417472 | 0.21082015 | 0.6265057 | 0.43941605 | 0.31905568 | 0.43992758 | 0.37230805 | 0.42313248 | 0.2436273 | 0.09361667 | 0.30848652 | 0.25807783 | 0.34295556 | 0.269539 |
| 16 | precision | 0.4801357 | 0.16153806 | 0.68421054 | 0.2962963 | 0.36842105 | 0.4117647 | 0.2 | 0.61538464 | 0.5294118 | 0.6666667 | 0.8333333 | 0.625 | 0.4 | 0.3888889 | 0.5714286 | 0.3888889 | 0.125 | 0.45 | 0.75 | 0.5 | 0.68421054 | 0.6 | 0.36 | 0.5882353 | 0.47368422 | 0.44444445 | 0.41666666 | 0.25 | 0.37037036 | 0.47058824 | 0.47058824 | 0.47058824 |
| 17 | r2 | 0.15468353 | 0.08616418 | 0.23773754 | 0.059921548 | 0.09488171 | 0.11552169 | -0.005347288 | 0.25765806 | 0.17877717 | 0.2148709 | 0.20418064 | 0.13590279 | 0.112279676 | 0.12654684 | 0.30599776 | 0.12363354 | -0.0074608084 | 0.16516033 | 0.091235876 | 0.080314115 | 0.3375659 | 0.21408764 | 0.17470525 | 0.24436095 | 0.26429367 | 0.21412618 | 0.11116447 | 0.006198136 | 0.13799961 | 0.14607891 | 0.19008578 | 0.108027495 |
| 18 | recall | 0.48250318 | 0.13061945 | 0.5 | 0.42105263 | 0.4117647 | 0.53846157 | 0.6666667 | 0.47058824 | 0.47368422 | 0.47058824 | 0.45454547 | 0.33333334 | 0.47058824 | 0.5833333 | 0.6666667 | 0.46666667 | 0.18181819 | 0.5625 | 0.5 | 0.33333334 | 0.65 | 0.4 | 0.6 | 0.5882353 | 0.6923077 | 0.6666667 | 0.33333334 | 0.18181819 | 0.47619048 | 0.5 | 0.5 | 0.3809524 |
| 19 | residual_deviance | 94.515434 | 17.4517 | 128.22488 | 123.88278 | 109.572975 | 85.568054 | 71.41365 | 91.61848 | 109.87321 | 96.60503 | 69.64659 | 98.89768 | 104.781654 | 77.189804 | 86.328865 | 95.84753 | 92.12382 | 95.709854 | 53.14544 | 86.99842 | 91.10136 | 88.15954 | 89.41297 | 90.7546 | 68.67195 | 99.63724 | 99.90219 | 88.879814 | 123.390205 | 96.65353 | 92.768364 | 128.70262 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:14:51 | 0.000 sec | 2 | .89E1 | 15.0 | 0.452055 | 0.452397 | 0.452453 | 0.015461 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:14:51 | 0.003 sec | 4 | .55E1 | 15.0 | 0.450579 | 0.451178 | 0.451044 | 0.015384 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:14:51 | 0.006 sec | 6 | .34E1 | 15.0 | 0.448256 | 0.449263 | 0.448825 | 0.015263 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:14:51 | 0.008 sec | 8 | .21E1 | 15.0 | 0.444637 | 0.446288 | 0.445365 | 0.015075 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:14:51 | 0.013 sec | 10 | .13E1 | 15.0 | 0.439164 | 0.441813 | 0.440117 | 0.014795 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:14:51 | 0.015 sec | 12 | .82E0 | 15.0 | 0.43123 | 0.435382 | 0.43248 | 0.014397 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:14:51 | 0.018 sec | 14 | .51E0 | 15.0 | 0.420556 | 0.426869 | 0.422135 | 0.013881 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:14:51 | 0.021 sec | 16 | .32E0 | 15.0 | 0.407879 | 0.417059 | 0.409727 | 0.013304 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:14:51 | 0.023 sec | 18 | .2E0 | 15.0 | 0.395196 | 0.407799 | 0.397171 | 0.012784 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:14:51 | 0.026 sec | 20 | .12E0 | 15.0 | 0.384668 | 0.400923 | 0.386676 | 0.012426 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:14:51 | 0.029 sec | 22 | .76E-1 | 15.0 | 0.377162 | 0.396978 | 0.37921 | 0.012244 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:14:51 | 0.032 sec | 24 | .47E-1 | 15.0 | 0.372304 | 0.39536 | 0.374459 | 0.012192 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:14:51 | 0.035 sec | 26 | .29E-1 | 15.0 | 0.369311 | 0.395146 | 0.371634 | 0.012217 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:14:51 | 0.037 sec | 28 | .18E-1 | 15.0 | 0.367504 | 0.39559 | 0.369337 | 0.012284 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:14:51 | 0.320 sec | 29 | None | NaN | 29.0 | 0.217892 | 0.183213 | 0.159624 | 0.785329 | 0.298953 | 8.318376 | 0.071282 | 0.224942 | 0.198117 | 0.10415 | 0.718441 | 0.251624 | 9.984615 | 0.085259 | ||||||
| 15 | 2021-07-15 20:14:51 | 0.040 sec | 30 | .11E-1 | 15.0 | 0.366426 | 0.396233 | 0.368326 | 0.012354 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:14:51 | 0.043 sec | 32 | .7E-2 | 15.0 | 0.365794 | 0.396852 | 0.368904 | 0.01278 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:14:51 | 0.045 sec | 34 | .44E-2 | 15.0 | 0.365432 | 0.397364 | 0.369594 | 0.01279 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:14:51 | 0.048 sec | 36 | .27E-2 | 15.0 | 0.365226 | 0.397769 | 0.369325 | 0.012817 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.572687 | 1.000000 | 0.277943 |
| 1 | Average_Transaction_Frequency | 0.248751 | 0.434358 | 0.120727 |
| 2 | Merchant_ID | 0.213032 | 0.371986 | 0.103391 |
| 3 | Minimum_Transaction_Amount | 0.198234 | 0.346148 | 0.096209 |
| 4 | Channel_ID | 0.171054 | 0.298687 | 0.083018 |
| 5 | Card_Type.1 | 0.140864 | 0.245970 | 0.068366 |
| 6 | Card_Type.0 | 0.139488 | 0.243568 | 0.067698 |
| 7 | Transaction_Amount | 0.116444 | 0.203329 | 0.056514 |
| 8 | Transaction_Date | 0.075442 | 0.131734 | 0.036614 |
| 9 | Average_Transaction_Amount | 0.054673 | 0.095468 | 0.026535 |
| 10 | Day | 0.053475 | 0.093376 | 0.025953 |
| 11 | City_ID | 0.033968 | 0.059313 | 0.016486 |
| 12 | Month | 0.031375 | 0.054786 | 0.015227 |
| 13 | Maximum_Transaction_Amount | 0.010959 | 0.019136 | 0.005319 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201454 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01123 ) | nlambda = 30, lambda.max = 8.8465, lambda.min = 0.01123, lambda.1s... | 14 | 14 | 30 | automl_training_py_630_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04795520922918298 RMSE: 0.21898677866296626 LogLoss: 0.18613039525961061 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938407 Residual deviance: 2898.4225149826566 AIC: 2928.4225149826566 AUC: 0.7682083809270199 AUCPR: 0.2898043028828212 Gini: 0.5364167618540399 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2650033236648337:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7027.0 | 291.0 | 0.0398 | (291.0/7318.0) |
| 1 | 1 | 269.0 | 199.0 | 0.5748 | (269.0/468.0) |
| 2 | Total | 7296.0 | 490.0 | 0.0719 | (560.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.265003 | 0.415449 | 149.0 |
| 1 | max f2 | 0.061162 | 0.441066 | 230.0 |
| 2 | max f0point5 | 0.322694 | 0.420627 | 121.0 |
| 3 | max accuracy | 0.574814 | 0.940534 | 9.0 |
| 4 | max precision | 0.822963 | 1.000000 | 0.0 |
| 5 | max recall | 0.021551 | 1.000000 | 378.0 |
| 6 | max specificity | 0.822963 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.265003 | 0.377264 | 149.0 |
| 8 | max min_per_class_accuracy | 0.041833 | 0.690171 | 285.0 |
| 9 | max mean_per_class_accuracy | 0.061162 | 0.713012 | 230.0 |
| 10 | max tns | 0.822963 | 7318.000000 | 0.0 |
| 11 | max fns | 0.822963 | 467.000000 | 0.0 |
| 12 | max fps | 0.002304 | 7318.000000 | 399.0 |
| 13 | max tps | 0.021551 | 468.000000 | 378.0 |
| 14 | max tnr | 0.822963 | 1.000000 | 0.0 |
| 15 | max fnr | 0.822963 | 0.997863 | 0.0 |
| 16 | max fpr | 0.002304 | 1.000000 | 399.0 |
| 17 | max tpr | 0.021551 | 1.000000 | 378.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.430010 | 8.318376 | 8.318376 | 0.500000 | 0.501315 | 0.500000 | 0.501315 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.391183 | 7.891793 | 8.105084 | 0.474359 | 0.409054 | 0.487179 | 0.455184 | 0.079060 | 0.162393 | 689.179268 | 710.508437 | 0.151461 |
| 2 | 3 | 0.030054 | 0.371138 | 6.612043 | 7.607404 | 0.397436 | 0.381510 | 0.457265 | 0.430626 | 0.066239 | 0.228632 | 561.204252 | 660.740375 | 0.211278 |
| 3 | 4 | 0.040072 | 0.350383 | 6.398751 | 7.305241 | 0.384615 | 0.359466 | 0.439103 | 0.412836 | 0.064103 | 0.292735 | 539.875082 | 630.524052 | 0.268821 |
| 4 | 5 | 0.050090 | 0.328466 | 6.398751 | 7.123943 | 0.384615 | 0.340933 | 0.428205 | 0.398456 | 0.064103 | 0.356838 | 539.875082 | 612.394258 | 0.326365 |
| 5 | 6 | 0.100051 | 0.061507 | 2.865456 | 4.997433 | 0.172237 | 0.151413 | 0.300385 | 0.275093 | 0.143162 | 0.500000 | 186.545602 | 399.743261 | 0.425526 |
| 6 | 7 | 0.150013 | 0.051119 | 0.769824 | 3.589436 | 0.046272 | 0.055136 | 0.215753 | 0.201837 | 0.038462 | 0.538462 | -23.017599 | 258.943625 | 0.413291 |
| 7 | 8 | 0.200103 | 0.046782 | 0.938483 | 2.925847 | 0.056410 | 0.048792 | 0.175866 | 0.163526 | 0.047009 | 0.585470 | -6.151655 | 192.584729 | 0.410012 |
| 8 | 9 | 0.300026 | 0.042637 | 0.898128 | 2.250520 | 0.053985 | 0.044491 | 0.135274 | 0.123882 | 0.089744 | 0.675214 | -10.187199 | 125.051955 | 0.399182 |
| 9 | 10 | 0.400077 | 0.039556 | 0.533914 | 1.821230 | 0.032092 | 0.041056 | 0.109470 | 0.103169 | 0.053419 | 0.728632 | -46.608626 | 82.123033 | 0.349567 |
| 10 | 11 | 0.500000 | 0.036882 | 0.534600 | 1.564103 | 0.032134 | 0.038254 | 0.094015 | 0.090196 | 0.053419 | 0.782051 | -46.540000 | 56.410256 | 0.300089 |
| 11 | 12 | 0.600051 | 0.034551 | 0.597983 | 1.403014 | 0.035944 | 0.035708 | 0.084332 | 0.081111 | 0.059829 | 0.841880 | -40.201661 | 40.301377 | 0.257294 |
| 12 | 13 | 0.699974 | 0.032300 | 0.684288 | 1.300414 | 0.041131 | 0.033438 | 0.078165 | 0.074305 | 0.068376 | 0.910256 | -31.571199 | 30.041402 | 0.223730 |
| 13 | 14 | 0.800026 | 0.029812 | 0.405774 | 1.188530 | 0.024390 | 0.031124 | 0.071440 | 0.068905 | 0.040598 | 0.950855 | -59.422556 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.026185 | 0.256608 | 1.085057 | 0.015424 | 0.028256 | 0.065220 | 0.064392 | 0.025641 | 0.976496 | -74.339200 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.001823 | 0.234922 | 1.000000 | 0.014121 | 0.021576 | 0.060108 | 0.060108 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04915576919755263 RMSE: 0.22171100378094144 LogLoss: 0.1882877893652975 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311443 Residual deviance: 733.1926517884684 AIC: 763.1926517884684 AUC: 0.7825650366633973 AUCPR: 0.2842224352876615 Gini: 0.5651300733267945 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.10207965888034737:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1719.0 | 111.0 | 0.0607 | (111.0/1830.0) |
| 1 | 1 | 60.0 | 57.0 | 0.5128 | (60.0/117.0) |
| 2 | Total | 1779.0 | 168.0 | 0.0878 | (171.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.102080 | 0.400000 | 137.0 |
| 1 | max f2 | 0.059333 | 0.462185 | 180.0 |
| 2 | max f0point5 | 0.415715 | 0.383142 | 32.0 |
| 3 | max accuracy | 0.456535 | 0.941962 | 11.0 |
| 4 | max precision | 0.549327 | 0.666667 | 2.0 |
| 5 | max recall | 0.023220 | 1.000000 | 373.0 |
| 6 | max specificity | 0.825608 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.102080 | 0.361010 | 137.0 |
| 8 | max min_per_class_accuracy | 0.042547 | 0.706011 | 247.0 |
| 9 | max mean_per_class_accuracy | 0.059333 | 0.732871 | 180.0 |
| 10 | max tns | 0.825608 | 1829.000000 | 0.0 |
| 11 | max fns | 0.825608 | 117.000000 | 0.0 |
| 12 | max fps | 0.002542 | 1830.000000 | 399.0 |
| 13 | max tps | 0.023220 | 117.000000 | 373.0 |
| 14 | max tnr | 0.825608 | 0.999454 | 0.0 |
| 15 | max fnr | 0.825608 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002542 | 1.000000 | 399.0 |
| 17 | max tpr | 0.023220 | 1.000000 | 373.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.48 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.439003 | 8.320513 | 8.320513 | 0.500000 | 0.490700 | 0.500000 | 0.490700 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.411313 | 8.758435 | 8.533859 | 0.526316 | 0.426610 | 0.512821 | 0.459476 | 0.085470 | 0.170940 | 775.843455 | 753.385930 | 0.160558 |
| 2 | 3 | 0.030303 | 0.388035 | 4.160256 | 7.051282 | 0.250000 | 0.399449 | 0.423729 | 0.439128 | 0.042735 | 0.213675 | 316.025641 | 605.128205 | 0.195096 |
| 3 | 4 | 0.040062 | 0.364472 | 5.255061 | 6.613741 | 0.315789 | 0.372600 | 0.397436 | 0.422922 | 0.051282 | 0.264957 | 425.506073 | 561.374096 | 0.239274 |
| 4 | 5 | 0.050334 | 0.345232 | 5.824359 | 6.452643 | 0.350000 | 0.352781 | 0.387755 | 0.408608 | 0.059829 | 0.324786 | 482.435897 | 545.264260 | 0.291999 |
| 5 | 6 | 0.100154 | 0.073101 | 3.602696 | 5.034977 | 0.216495 | 0.217472 | 0.302564 | 0.313530 | 0.179487 | 0.504274 | 260.269627 | 403.497699 | 0.429957 |
| 6 | 7 | 0.149974 | 0.053870 | 1.372456 | 3.818318 | 0.082474 | 0.060461 | 0.229452 | 0.229463 | 0.068376 | 0.572650 | 37.245572 | 281.831753 | 0.449699 |
| 7 | 8 | 0.200308 | 0.047951 | 0.339613 | 2.944181 | 0.020408 | 0.050650 | 0.176923 | 0.184530 | 0.017094 | 0.589744 | -66.038723 | 194.418146 | 0.414334 |
| 8 | 9 | 0.299949 | 0.043080 | 1.029342 | 2.308087 | 0.061856 | 0.045424 | 0.138699 | 0.138320 | 0.102564 | 0.692308 | 2.934179 | 130.808746 | 0.417444 |
| 9 | 10 | 0.400103 | 0.039987 | 0.853386 | 1.943945 | 0.051282 | 0.041528 | 0.116816 | 0.114091 | 0.085470 | 0.777778 | -14.661407 | 94.394523 | 0.401821 |
| 10 | 11 | 0.500257 | 0.037350 | 0.341354 | 1.623098 | 0.020513 | 0.038655 | 0.097536 | 0.098988 | 0.034188 | 0.811966 | -65.864563 | 62.309798 | 0.331638 |
| 11 | 12 | 0.599897 | 0.034984 | 0.600449 | 1.453240 | 0.036082 | 0.036208 | 0.087329 | 0.088561 | 0.059829 | 0.871795 | -39.955062 | 45.324025 | 0.289281 |
| 12 | 13 | 0.700051 | 0.032634 | 0.426693 | 1.306375 | 0.025641 | 0.033828 | 0.078503 | 0.080730 | 0.042735 | 0.914530 | -57.330703 | 30.637545 | 0.228191 |
| 13 | 14 | 0.799692 | 0.030412 | 0.514671 | 1.207730 | 0.030928 | 0.031604 | 0.072575 | 0.074609 | 0.051282 | 0.965812 | -48.532910 | 20.773018 | 0.176741 |
| 14 | 15 | 0.899846 | 0.027063 | 0.170677 | 1.092305 | 0.010256 | 0.028769 | 0.065639 | 0.069507 | 0.017094 | 0.982906 | -82.932281 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.002518 | 0.170677 | 1.000000 | 0.010256 | 0.022690 | 0.060092 | 0.064818 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04826671454692414 RMSE: 0.2196968696793929 LogLoss: 0.1875092806264626 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.0954876046826 Residual deviance: 2919.8945179152756 AIC: 2949.8945179152756 AUC: 0.7562115600684881 AUCPR: 0.2750594684394488 Gini: 0.5124231201369762 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.177549952372197:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6998.0 | 320.0 | 0.0437 | (320.0/7318.0) |
| 1 | 1 | 263.0 | 205.0 | 0.562 | (263.0/468.0) |
| 2 | Total | 7261.0 | 525.0 | 0.0749 | (583.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.177550 | 0.412890 | 169.0 |
| 1 | max f2 | 0.071327 | 0.433583 | 211.0 |
| 2 | max f0point5 | 0.314910 | 0.415129 | 128.0 |
| 3 | max accuracy | 0.563770 | 0.940663 | 10.0 |
| 4 | max precision | 0.563770 | 0.750000 | 10.0 |
| 5 | max recall | 0.020381 | 1.000000 | 380.0 |
| 6 | max specificity | 0.837233 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.177550 | 0.373745 | 169.0 |
| 8 | max min_per_class_accuracy | 0.041584 | 0.681624 | 283.0 |
| 9 | max mean_per_class_accuracy | 0.058933 | 0.708527 | 228.0 |
| 10 | max tns | 0.837233 | 7317.000000 | 0.0 |
| 11 | max fns | 0.837233 | 468.000000 | 0.0 |
| 12 | max fps | 0.002461 | 7318.000000 | 399.0 |
| 13 | max tps | 0.020381 | 468.000000 | 380.0 |
| 14 | max tnr | 0.837233 | 0.999863 | 0.0 |
| 15 | max fnr | 0.837233 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002461 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020381 | 1.000000 | 380.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.431192 | 7.678501 | 7.678501 | 0.461538 | 0.501617 | 0.461538 | 0.501617 | 0.076923 | 0.076923 | 667.850099 | 667.850099 | 0.071184 |
| 1 | 2 | 0.020036 | 0.390026 | 7.465209 | 7.571855 | 0.448718 | 0.408011 | 0.455128 | 0.454814 | 0.074786 | 0.151709 | 646.520929 | 657.185514 | 0.140094 |
| 2 | 3 | 0.030054 | 0.370602 | 7.251918 | 7.465209 | 0.435897 | 0.380503 | 0.448718 | 0.430044 | 0.072650 | 0.224359 | 625.191760 | 646.520929 | 0.206731 |
| 3 | 4 | 0.040072 | 0.350886 | 5.972167 | 7.091949 | 0.358974 | 0.359054 | 0.426282 | 0.412296 | 0.059829 | 0.284188 | 497.216743 | 609.194883 | 0.259728 |
| 4 | 5 | 0.050090 | 0.326185 | 6.612043 | 6.995968 | 0.397436 | 0.339013 | 0.420513 | 0.397640 | 0.066239 | 0.350427 | 561.204252 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.061365 | 2.779920 | 4.890650 | 0.167095 | 0.151392 | 0.293967 | 0.274674 | 0.138889 | 0.489316 | 177.992002 | 389.064986 | 0.414159 |
| 6 | 7 | 0.150013 | 0.051110 | 0.812592 | 3.532461 | 0.048843 | 0.055197 | 0.212329 | 0.201578 | 0.040598 | 0.529915 | -18.740799 | 253.246107 | 0.404197 |
| 7 | 8 | 0.200103 | 0.046901 | 0.895825 | 2.872456 | 0.053846 | 0.048832 | 0.172657 | 0.163342 | 0.044872 | 0.574786 | -10.417488 | 187.245592 | 0.398645 |
| 8 | 9 | 0.300026 | 0.042580 | 0.812592 | 2.186422 | 0.048843 | 0.044542 | 0.131421 | 0.123776 | 0.081197 | 0.655983 | -18.740799 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.039655 | 0.555270 | 1.778504 | 0.033376 | 0.041088 | 0.106902 | 0.103097 | 0.055556 | 0.711538 | -44.472971 | 77.850352 | 0.331380 |
| 10 | 11 | 0.500000 | 0.036923 | 0.577368 | 1.538462 | 0.034704 | 0.038247 | 0.092474 | 0.090137 | 0.057692 | 0.769231 | -42.263200 | 53.846154 | 0.286449 |
| 11 | 12 | 0.600051 | 0.034615 | 0.576627 | 1.378087 | 0.034660 | 0.035745 | 0.082834 | 0.081068 | 0.057692 | 0.826923 | -42.337316 | 37.808713 | 0.241381 |
| 12 | 13 | 0.699974 | 0.032339 | 0.684288 | 1.279046 | 0.041131 | 0.033510 | 0.076881 | 0.074279 | 0.068376 | 0.895299 | -31.571199 | 27.904571 | 0.207816 |
| 13 | 14 | 0.800026 | 0.029901 | 0.491201 | 1.180518 | 0.029525 | 0.031168 | 0.070958 | 0.068888 | 0.049145 | 0.944444 | -50.879936 | 18.051765 | 0.153655 |
| 14 | 15 | 0.899949 | 0.026267 | 0.320760 | 1.085057 | 0.019280 | 0.028333 | 0.065220 | 0.064385 | 0.032051 | 0.976496 | -67.924000 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.001934 | 0.234922 | 1.000000 | 0.014121 | 0.021669 | 0.060108 | 0.060111 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9341169 | 0.020418547 | 0.93846154 | 0.8923077 | 0.93846154 | 0.9423077 | 0.9423077 | 0.9307692 | 0.95384616 | 0.9461538 | 0.9230769 | 0.9153846 | 0.9423077 | 0.91923076 | 0.9346154 | 0.9576923 | 0.90384614 | 0.9307692 | 0.95752895 | 0.94208497 | 0.9498069 | 0.9266409 | 0.96138996 | 0.93822396 | 0.9111969 | 0.87258685 | 0.9189189 | 0.9459459 | 0.93822396 | 0.93822396 | 0.96138996 | 0.9498069 |
| 1 | auc | 0.76433396 | 0.06758016 | 0.7407407 | 0.78095657 | 0.77003145 | 0.684522 | 0.77864784 | 0.85436034 | 0.6898522 | 0.6672109 | 0.7616501 | 0.81625664 | 0.8657103 | 0.6985455 | 0.7460973 | 0.8159452 | 0.76033056 | 0.6476 | 0.9234143 | 0.7898111 | 0.7962213 | 0.7044952 | 0.7409639 | 0.747541 | 0.8294461 | 0.6820084 | 0.7366255 | 0.81894934 | 0.6546448 | 0.8578026 | 0.79520917 | 0.7744288 |
| 2 | err | 0.06588308 | 0.020418547 | 0.06153846 | 0.10769231 | 0.06153846 | 0.057692308 | 0.057692308 | 0.06923077 | 0.046153847 | 0.053846154 | 0.07692308 | 0.08461539 | 0.057692308 | 0.08076923 | 0.06538462 | 0.042307694 | 0.09615385 | 0.06923077 | 0.042471044 | 0.057915058 | 0.05019305 | 0.07335907 | 0.038610037 | 0.06177606 | 0.08880309 | 0.12741312 | 0.08108108 | 0.054054055 | 0.06177606 | 0.06177606 | 0.038610037 | 0.05019305 |
| 3 | err_count | 17.1 | 5.300293 | 16.0 | 28.0 | 16.0 | 15.0 | 15.0 | 18.0 | 12.0 | 14.0 | 20.0 | 22.0 | 15.0 | 21.0 | 17.0 | 11.0 | 25.0 | 18.0 | 11.0 | 15.0 | 13.0 | 19.0 | 10.0 | 16.0 | 23.0 | 33.0 | 21.0 | 14.0 | 16.0 | 16.0 | 10.0 | 13.0 |
| 4 | f0point5 | 0.45921597 | 0.11283543 | 0.5194805 | 0.37037036 | 0.5194805 | 0.40983605 | 0.6666667 | 0.34653464 | 0.48076922 | 0.49019608 | 0.5319149 | 0.30927834 | 0.64220184 | 0.4950495 | 0.46296296 | 0.5797101 | 0.31914893 | 0.22727273 | 0.5208333 | 0.390625 | 0.44642857 | 0.57471263 | 0.2777778 | 0.46666667 | 0.39855072 | 0.2734375 | 0.38043478 | 0.47945204 | 0.46666667 | 0.5445545 | 0.5681818 | 0.61728394 |
| 5 | f1 | 0.45218408 | 0.10743512 | 0.5 | 0.41666666 | 0.5 | 0.4 | 0.61538464 | 0.4375 | 0.45454547 | 0.41666666 | 0.5 | 0.3529412 | 0.6511628 | 0.4878049 | 0.37037036 | 0.5925926 | 0.3243243 | 0.25 | 0.47619048 | 0.4 | 0.4347826 | 0.51282054 | 0.16666667 | 0.46666667 | 0.4888889 | 0.29787233 | 0.4 | 0.5 | 0.46666667 | 0.57894737 | 0.5 | 0.6060606 |
| 6 | f2 | 0.45617127 | 0.11750795 | 0.48192772 | 0.47619048 | 0.48192772 | 0.390625 | 0.5714286 | 0.59322035 | 0.43103448 | 0.36231884 | 0.4716981 | 0.41095892 | 0.6603774 | 0.48076922 | 0.30864197 | 0.6060606 | 0.32967034 | 0.2777778 | 0.4385965 | 0.40983605 | 0.42372882 | 0.46296296 | 0.11904762 | 0.46666667 | 0.6321839 | 0.3271028 | 0.42168674 | 0.52238804 | 0.46666667 | 0.6179775 | 0.44642857 | 0.5952381 |
| 7 | lift_top_group | 7.6740212 | 5.244551 | 15.294118 | 4.5614033 | 5.098039 | 6.6666665 | 11.818182 | 9.62963 | 14.444445 | 0.0 | 11.818182 | 0.0 | 4.126984 | 8.253968 | 14.444445 | 13.333333 | 9.62963 | 0.0 | 14.388889 | 7.1944447 | 7.1944447 | 3.7536232 | 8.633333 | 11.511111 | 0.0 | 0.0 | 5.3958335 | 19.923077 | 5.7555556 | 5.0784316 | 7.1944447 | 5.0784316 |
| 8 | logloss | 0.18604417 | 0.035447374 | 0.19121675 | 0.22268829 | 0.1958424 | 0.17379403 | 0.21350095 | 0.118780196 | 0.15441021 | 0.19587485 | 0.23401673 | 0.17565629 | 0.18874614 | 0.23738955 | 0.22281834 | 0.14015435 | 0.23591104 | 0.15885577 | 0.14566053 | 0.16177696 | 0.15326405 | 0.2529977 | 0.16107391 | 0.18093568 | 0.16841044 | 0.25780854 | 0.20075047 | 0.14703305 | 0.18465781 | 0.17191944 | 0.15745294 | 0.17792775 |
| 9 | max_per_class_error | 0.5335823 | 0.14280434 | 0.5294118 | 0.47368422 | 0.5294118 | 0.61538464 | 0.45454547 | 0.22222222 | 0.5833333 | 0.6666667 | 0.54545456 | 0.53846157 | 0.33333334 | 0.52380955 | 0.7222222 | 0.3846154 | 0.6666667 | 0.7 | 0.5833333 | 0.5833333 | 0.5833333 | 0.5652174 | 0.9 | 0.53333336 | 0.21428572 | 0.65 | 0.5625 | 0.46153846 | 0.53333336 | 0.3529412 | 0.5833333 | 0.4117647 |
| 10 | mcc | 0.4270241 | 0.10603822 | 0.46838892 | 0.36995733 | 0.46838892 | 0.37007472 | 0.5904364 | 0.45966154 | 0.432609 | 0.40433297 | 0.46149588 | 0.32058987 | 0.619934 | 0.44414756 | 0.36277738 | 0.5707481 | 0.27272066 | 0.21811141 | 0.45964268 | 0.36995202 | 0.40899542 | 0.4836832 | 0.21125971 | 0.4338798 | 0.49048057 | 0.23264225 | 0.35837993 | 0.47293204 | 0.4338798 | 0.54956335 | 0.49147138 | 0.5795864 |
| 11 | mean_per_class_accuracy | 0.7150822 | 0.067863725 | 0.7208908 | 0.7237388 | 0.7208908 | 0.67813766 | 0.76222306 | 0.8570164 | 0.6982527 | 0.6585034 | 0.710466 | 0.7004049 | 0.8165969 | 0.7171747 | 0.6306244 | 0.79554653 | 0.63980716 | 0.628 | 0.70023614 | 0.692139 | 0.69618756 | 0.7046794 | 0.547992 | 0.71693987 | 0.8520408 | 0.633159 | 0.69405866 | 0.75297064 | 0.71693987 | 0.80286825 | 0.70226043 | 0.78172094 |
| 12 | mean_per_class_error | 0.28491777 | 0.067863725 | 0.27910918 | 0.27626118 | 0.27910918 | 0.32186234 | 0.23777694 | 0.14298362 | 0.30174732 | 0.3414966 | 0.289534 | 0.29959515 | 0.18340307 | 0.28282526 | 0.3693756 | 0.20445344 | 0.36019284 | 0.372 | 0.29976383 | 0.307861 | 0.3038124 | 0.29532057 | 0.45200804 | 0.2830601 | 0.14795919 | 0.36684102 | 0.30594134 | 0.2470294 | 0.2830601 | 0.19713174 | 0.29773954 | 0.21827905 |
| 13 | mse | 0.047860347 | 0.010424612 | 0.04920481 | 0.06008983 | 0.050560348 | 0.042924218 | 0.056130942 | 0.028576637 | 0.03682674 | 0.0495488 | 0.06214525 | 0.04450592 | 0.05213755 | 0.061822746 | 0.057593036 | 0.03471764 | 0.06134459 | 0.03691755 | 0.03756467 | 0.040487334 | 0.037945308 | 0.06792265 | 0.038882457 | 0.046012864 | 0.045662466 | 0.06849263 | 0.052537035 | 0.036311552 | 0.046849206 | 0.04641244 | 0.03955521 | 0.046127975 |
| 14 | null_deviance | 118.036514 | 21.62641 | 125.73113 | 136.7752 | 125.73113 | 103.75808 | 153.41394 | 81.936905 | 98.28862 | 114.7255 | 153.41394 | 103.75808 | 147.85797 | 147.85797 | 131.24835 | 103.75808 | 131.24835 | 87.37807 | 98.16257 | 98.16257 | 98.16257 | 158.85994 | 87.25086 | 114.60118 | 109.11213 | 142.19029 | 120.09978 | 103.63261 | 114.60118 | 125.607956 | 98.16257 | 125.607956 |
| 15 | pr_auc | 0.3067676 | 0.11303797 | 0.47161382 | 0.27299705 | 0.34688574 | 0.21711746 | 0.55498236 | 0.21941262 | 0.3170482 | 0.20136823 | 0.43550366 | 0.17553599 | 0.45816377 | 0.38387063 | 0.35979998 | 0.4428554 | 0.2772114 | 0.08762756 | 0.35167614 | 0.26294932 | 0.29069614 | 0.29339722 | 0.09735575 | 0.29088265 | 0.25225425 | 0.16480054 | 0.22181413 | 0.44512713 | 0.221926 | 0.4062002 | 0.31054837 | 0.3714068 |
| 16 | precision | 0.4734895 | 0.124962255 | 0.53333336 | 0.3448276 | 0.53333336 | 0.41666666 | 0.7058824 | 0.3043478 | 0.5 | 0.5555556 | 0.5555556 | 0.2857143 | 0.6363636 | 0.5 | 0.5555556 | 0.5714286 | 0.31578946 | 0.21428572 | 0.5555556 | 0.3846154 | 0.45454547 | 0.625 | 0.5 | 0.46666667 | 0.3548387 | 0.25925925 | 0.36842105 | 0.46666667 | 0.46666667 | 0.52380955 | 0.625 | 0.625 |
| 17 | r2 | 0.14197108 | 0.08183449 | 0.1948087 | 0.1128909 | 0.1726266 | 0.09633226 | 0.2753148 | 0.14485145 | 0.16347861 | 0.08857169 | 0.19766633 | 0.06303325 | 0.2977688 | 0.16732068 | 0.106223784 | 0.2691023 | 0.048004095 | 0.0017494381 | 0.14983958 | 0.08369407 | 0.14122494 | 0.16058962 | -0.047499634 | 0.15666974 | 0.106972635 | 0.038796194 | 0.0935605 | 0.23833174 | 0.14134108 | 0.24322006 | 0.10478974 | 0.24785838 |
| 18 | recall | 0.46641764 | 0.14280434 | 0.47058824 | 0.5263158 | 0.47058824 | 0.3846154 | 0.54545456 | 0.7777778 | 0.41666666 | 0.33333334 | 0.45454547 | 0.46153846 | 0.6666667 | 0.47619048 | 0.2777778 | 0.61538464 | 0.33333334 | 0.3 | 0.41666666 | 0.41666666 | 0.41666666 | 0.4347826 | 0.1 | 0.46666667 | 0.78571427 | 0.35 | 0.4375 | 0.53846157 | 0.46666667 | 0.64705884 | 0.41666666 | 0.5882353 |
| 19 | residual_deviance | 96.57486 | 18.431114 | 99.43271 | 115.79791 | 101.83805 | 90.3729 | 111.02049 | 61.7657 | 80.29331 | 101.85492 | 121.6887 | 91.34127 | 98.14799 | 123.442566 | 115.86554 | 72.880264 | 122.67374 | 82.605 | 75.452156 | 83.80046 | 79.39077 | 131.0528 | 83.43629 | 93.72468 | 87.23661 | 133.54483 | 103.98874 | 76.16312 | 95.65275 | 89.05427 | 81.56062 | 92.16657 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:15:02 | 0.000 sec | 2 | .88E1 | 14 | 0.452090 | 0.452139 | 0.452449 | 0.015070 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:15:02 | 0.005 sec | 4 | .55E1 | 14 | 0.450637 | 0.450766 | 0.451061 | 0.015005 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:15:02 | 0.009 sec | 6 | .34E1 | 14 | 0.448351 | 0.448609 | 0.448877 | 0.014904 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:15:02 | 0.012 sec | 8 | .21E1 | 14 | 0.444796 | 0.445257 | 0.445474 | 0.014748 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:15:02 | 0.016 sec | 10 | .13E1 | 14 | 0.439428 | 0.440209 | 0.440324 | 0.014518 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:15:02 | 0.020 sec | 12 | .82E0 | 15 | 0.431675 | 0.432946 | 0.432857 | 0.014197 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:15:02 | 0.024 sec | 14 | .51E0 | 15 | 0.421318 | 0.423297 | 0.422813 | 0.013793 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:15:02 | 0.029 sec | 16 | .32E0 | 15 | 0.409167 | 0.412066 | 0.410909 | 0.013368 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:15:02 | 0.033 sec | 18 | .2E0 | 15 | 0.397248 | 0.401145 | 0.399095 | 0.013029 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:15:02 | 0.038 sec | 20 | .12E0 | 15 | 0.387633 | 0.392364 | 0.389500 | 0.012849 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:15:02 | 0.043 sec | 22 | .75E-1 | 15 | 0.381014 | 0.386232 | 0.382934 | 0.012815 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:15:02 | 0.048 sec | 24 | .47E-1 | 15 | 0.376908 | 0.382229 | 0.378960 | 0.012862 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:15:02 | 0.055 sec | 26 | .29E-1 | 15 | 0.374487 | 0.379617 | 0.376743 | 0.012936 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:15:02 | 0.059 sec | 28 | .18E-1 | 15 | 0.373078 | 0.377840 | 0.375573 | 0.013003 | 0.0 | 28.0 | 0.218987 | 0.18613 | 0.151159 | 0.768208 | 0.289804 | 8.318376 | 0.071924 | 0.221711 | 0.188288 | 0.129699 | 0.782565 | 0.284222 | 8.320513 | 0.087827 | |
| 14 | 2021-07-15 20:15:02 | 0.064 sec | 30 | .11E-1 | 15 | 0.372261 | 0.376576 | 0.374997 | 0.013055 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:15:02 | 0.068 sec | 32 | .7E-2 | 15 | 0.371792 | 0.375663 | 0.375534 | 0.013315 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:15:02 | 0.071 sec | 34 | .43E-2 | 15 | 0.371531 | 0.375009 | 0.375391 | 0.013325 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:15:02 | 0.074 sec | 35 | .27E-2 | 15 | 0.371396 | 0.374559 | 0.376503 | 0.013350 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.546816 | 1.000000 | 0.286255 |
| 1 | Average_Transaction_Frequency | 0.216105 | 0.395205 | 0.113129 |
| 2 | Channel_ID | 0.188930 | 0.345509 | 0.098903 |
| 3 | Merchant_ID | 0.166398 | 0.304303 | 0.087108 |
| 4 | Minimum_Transaction_Amount | 0.164470 | 0.300777 | 0.086099 |
| 5 | Card_Type.1 | 0.121743 | 0.222639 | 0.063732 |
| 6 | Card_Type.0 | 0.120470 | 0.220312 | 0.063065 |
| 7 | Transaction_Amount | 0.090118 | 0.164806 | 0.047176 |
| 8 | Transaction_Date | 0.070361 | 0.128675 | 0.036834 |
| 9 | Month | 0.065166 | 0.119173 | 0.034114 |
| 10 | Day | 0.052268 | 0.095585 | 0.027362 |
| 11 | Average_Transaction_Amount | 0.042404 | 0.077548 | 0.022198 |
| 12 | Maximum_Transaction_Amount | 0.037131 | 0.067905 | 0.019438 |
| 13 | City_ID | 0.027864 | 0.050956 | 0.014586 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201505 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01107 ) | nlambda = 30, lambda.max = 8.7242, lambda.min = 0.01107, lambda.1s... | 14 | 14 | 30 | automl_training_py_659_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04784294880142132 RMSE: 0.21873031066000276 LogLoss: 0.1852513621363274 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793836 Residual deviance: 2884.7342111868893 AIC: 2914.7342111868893 AUC: 0.7744249047542298 AUCPR: 0.29639032194048526 Gini: 0.5488498095084595 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.26895357597765096:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7025.0 | 293.0 | 0.04 | (293.0/7318.0) |
| 1 | 1 | 266.0 | 202.0 | 0.5684 | (266.0/468.0) |
| 2 | Total | 7291.0 | 495.0 | 0.0718 | (559.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.268954 | 0.419522 | 149.0 |
| 1 | max f2 | 0.061569 | 0.447020 | 231.0 |
| 2 | max f0point5 | 0.312366 | 0.423729 | 124.0 |
| 3 | max accuracy | 0.443413 | 0.940791 | 41.0 |
| 4 | max precision | 0.845067 | 1.000000 | 0.0 |
| 5 | max recall | 0.020434 | 1.000000 | 377.0 |
| 6 | max specificity | 0.845067 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.268954 | 0.381461 | 149.0 |
| 8 | max min_per_class_accuracy | 0.041389 | 0.690626 | 288.0 |
| 9 | max mean_per_class_accuracy | 0.060127 | 0.719366 | 234.0 |
| 10 | max tns | 0.845067 | 7318.000000 | 0.0 |
| 11 | max fns | 0.845067 | 467.000000 | 0.0 |
| 12 | max fps | 0.001545 | 7318.000000 | 399.0 |
| 13 | max tps | 0.020434 | 468.000000 | 377.0 |
| 14 | max tnr | 0.845067 | 1.000000 | 0.0 |
| 15 | max fnr | 0.845067 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001545 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020434 | 1.000000 | 377.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.424119 | 8.958251 | 8.958251 | 0.538462 | 0.507638 | 0.538462 | 0.507638 | 0.089744 | 0.089744 | 795.825115 | 795.825115 | 0.084824 |
| 1 | 2 | 0.020036 | 0.391858 | 7.678501 | 8.318376 | 0.461538 | 0.406639 | 0.500000 | 0.457139 | 0.076923 | 0.166667 | 667.850099 | 731.837607 | 0.156008 |
| 2 | 3 | 0.030054 | 0.362138 | 7.251918 | 7.962890 | 0.435897 | 0.374981 | 0.478632 | 0.429753 | 0.072650 | 0.239316 | 625.191760 | 696.288991 | 0.222645 |
| 3 | 4 | 0.040072 | 0.345568 | 6.185459 | 7.518532 | 0.371795 | 0.353759 | 0.451923 | 0.410754 | 0.061966 | 0.301282 | 518.545913 | 651.853222 | 0.277915 |
| 4 | 5 | 0.050090 | 0.319802 | 5.332292 | 7.081284 | 0.320513 | 0.333901 | 0.425641 | 0.395384 | 0.053419 | 0.354701 | 433.229235 | 608.128424 | 0.324091 |
| 5 | 6 | 0.100051 | 0.066227 | 2.993760 | 5.040146 | 0.179949 | 0.161790 | 0.302953 | 0.278737 | 0.149573 | 0.504274 | 199.376002 | 404.014571 | 0.430073 |
| 6 | 7 | 0.150013 | 0.051726 | 0.940896 | 3.674899 | 0.056555 | 0.057232 | 0.220890 | 0.204965 | 0.047009 | 0.551282 | -5.910399 | 267.489902 | 0.426931 |
| 7 | 8 | 0.200103 | 0.047061 | 0.554558 | 2.893812 | 0.033333 | 0.049225 | 0.173941 | 0.165980 | 0.027778 | 0.579060 | -44.544160 | 189.381247 | 0.403192 |
| 8 | 9 | 0.300026 | 0.042359 | 0.962280 | 2.250520 | 0.057841 | 0.044398 | 0.135274 | 0.125487 | 0.096154 | 0.675214 | -3.771999 | 125.051955 | 0.399182 |
| 9 | 10 | 0.400077 | 0.039131 | 0.512557 | 1.815889 | 0.030809 | 0.040643 | 0.109149 | 0.104270 | 0.051282 | 0.726496 | -48.744281 | 81.588948 | 0.347294 |
| 10 | 11 | 0.500000 | 0.036385 | 0.705672 | 1.594017 | 0.042416 | 0.037722 | 0.095813 | 0.090970 | 0.070513 | 0.797009 | -29.432799 | 59.401709 | 0.316003 |
| 11 | 12 | 0.600051 | 0.034121 | 0.704766 | 1.445745 | 0.042362 | 0.035205 | 0.086901 | 0.081672 | 0.070513 | 0.867521 | -29.523386 | 44.574516 | 0.284575 |
| 12 | 13 | 0.699974 | 0.031615 | 0.427680 | 1.300414 | 0.025707 | 0.032844 | 0.078165 | 0.074702 | 0.042735 | 0.910256 | -57.232000 | 30.041402 | 0.223730 |
| 13 | 14 | 0.800026 | 0.029046 | 0.405774 | 1.188530 | 0.024390 | 0.030417 | 0.071440 | 0.069164 | 0.040598 | 0.950855 | -59.422556 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.025242 | 0.299376 | 1.089806 | 0.017995 | 0.027367 | 0.065506 | 0.064523 | 0.029915 | 0.980769 | -70.062400 | 8.980580 | 0.085989 |
| 15 | 16 | 1.000000 | 0.001271 | 0.192209 | 1.000000 | 0.011553 | 0.020396 | 0.060108 | 0.060108 | 0.019231 | 1.000000 | -80.779105 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04937967857284716 RMSE: 0.2222153877949211 LogLoss: 0.19046570241551358 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311455 Residual deviance: 741.6734452060099 AIC: 771.6734452060099 AUC: 0.7630353556583065 AUCPR: 0.260756414887221 Gini: 0.526070711316613 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.15544090294337326:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1735.0 | 95.0 | 0.0519 | (95.0/1830.0) |
| 1 | 1 | 65.0 | 52.0 | 0.5556 | (65.0/117.0) |
| 2 | Total | 1800.0 | 147.0 | 0.0822 | (160.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.155441 | 0.393939 | 124.0 |
| 1 | max f2 | 0.079115 | 0.433071 | 142.0 |
| 2 | max f0point5 | 0.317728 | 0.385996 | 90.0 |
| 3 | max accuracy | 0.526075 | 0.940935 | 3.0 |
| 4 | max precision | 0.656006 | 1.000000 | 0.0 |
| 5 | max recall | 0.021835 | 1.000000 | 364.0 |
| 6 | max specificity | 0.656006 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.155441 | 0.353101 | 124.0 |
| 8 | max min_per_class_accuracy | 0.041647 | 0.692308 | 238.0 |
| 9 | max mean_per_class_accuracy | 0.050402 | 0.706838 | 194.0 |
| 10 | max tns | 0.656006 | 1830.000000 | 0.0 |
| 11 | max fns | 0.656006 | 116.000000 | 0.0 |
| 12 | max fps | 0.001394 | 1830.000000 | 399.0 |
| 13 | max tps | 0.021835 | 117.000000 | 364.0 |
| 14 | max tnr | 0.656006 | 1.000000 | 0.0 |
| 15 | max fnr | 0.656006 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001394 | 1.000000 | 399.0 |
| 17 | max tpr | 0.021835 | 1.000000 | 364.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.14 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.436309 | 8.320513 | 8.320513 | 0.500000 | 0.492288 | 0.500000 | 0.492288 | 0.085470 | 0.085470 | 732.051282 | 732.051282 | 0.080006 |
| 1 | 2 | 0.020031 | 0.390355 | 3.503374 | 5.973702 | 0.210526 | 0.413723 | 0.358974 | 0.454013 | 0.034188 | 0.119658 | 250.337382 | 497.370151 | 0.105997 |
| 2 | 3 | 0.030303 | 0.370905 | 7.488462 | 6.487179 | 0.450000 | 0.382026 | 0.389831 | 0.429610 | 0.076923 | 0.196581 | 648.846154 | 548.717949 | 0.176909 |
| 3 | 4 | 0.040062 | 0.350678 | 6.130904 | 6.400394 | 0.368421 | 0.360150 | 0.384615 | 0.412691 | 0.059829 | 0.256410 | 513.090418 | 540.039448 | 0.230181 |
| 4 | 5 | 0.050334 | 0.331040 | 6.656410 | 6.452643 | 0.400000 | 0.342014 | 0.387755 | 0.398267 | 0.068376 | 0.324786 | 565.641026 | 545.264260 | 0.291999 |
| 5 | 6 | 0.100154 | 0.063267 | 3.259582 | 4.864300 | 0.195876 | 0.185370 | 0.292308 | 0.292364 | 0.162393 | 0.487179 | 225.958234 | 386.429980 | 0.411770 |
| 6 | 7 | 0.149974 | 0.051084 | 0.514671 | 3.419389 | 0.030928 | 0.055561 | 0.205479 | 0.213700 | 0.025641 | 0.512821 | -48.532910 | 241.938883 | 0.386045 |
| 7 | 8 | 0.200308 | 0.046981 | 0.849032 | 2.773504 | 0.051020 | 0.048690 | 0.166667 | 0.172236 | 0.042735 | 0.555556 | -15.096808 | 177.350427 | 0.377960 |
| 8 | 9 | 0.299949 | 0.042071 | 1.115120 | 2.222603 | 0.067010 | 0.044308 | 0.133562 | 0.129739 | 0.111111 | 0.666667 | 11.512027 | 122.260274 | 0.390164 |
| 9 | 10 | 0.400103 | 0.039198 | 0.853386 | 1.879859 | 0.051282 | 0.040720 | 0.112965 | 0.107456 | 0.085470 | 0.752137 | -14.661407 | 87.985912 | 0.374541 |
| 10 | 11 | 0.500257 | 0.036588 | 0.256016 | 1.554757 | 0.015385 | 0.037838 | 0.093429 | 0.093518 | 0.025641 | 0.777778 | -74.398422 | 55.475702 | 0.295264 |
| 11 | 12 | 0.599897 | 0.034245 | 0.686228 | 1.410498 | 0.041237 | 0.035412 | 0.084760 | 0.083867 | 0.068376 | 0.846154 | -31.377214 | 41.049789 | 0.262001 |
| 12 | 13 | 0.700051 | 0.031913 | 0.597370 | 1.294166 | 0.035897 | 0.033140 | 0.077770 | 0.076609 | 0.059829 | 0.905983 | -40.262985 | 29.416634 | 0.219098 |
| 13 | 14 | 0.799692 | 0.029065 | 0.257335 | 1.164979 | 0.015464 | 0.030480 | 0.070006 | 0.070862 | 0.025641 | 0.931624 | -74.266455 | 16.497867 | 0.140367 |
| 14 | 15 | 0.899846 | 0.024459 | 0.426693 | 1.082806 | 0.025641 | 0.027041 | 0.065068 | 0.065985 | 0.042735 | 0.974359 | -57.330703 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.001394 | 0.256016 | 1.000000 | 0.015385 | 0.019749 | 0.060092 | 0.061354 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048182524797344896 RMSE: 0.21950518170955532 LogLoss: 0.1867598780750042 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.5404320663447 Residual deviance: 2908.2248213839653 AIC: 2938.2248213839653 AUC: 0.7619527310016515 AUCPR: 0.2823101284330826 Gini: 0.523905462003303 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.252175378898569:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7016.0 | 302.0 | 0.0413 | (302.0/7318.0) |
| 1 | 1 | 266.0 | 202.0 | 0.5684 | (266.0/468.0) |
| 2 | Total | 7282.0 | 504.0 | 0.073 | (568.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.252175 | 0.415638 | 153.0 |
| 1 | max f2 | 0.066105 | 0.442895 | 226.0 |
| 2 | max f0point5 | 0.312338 | 0.411932 | 123.0 |
| 3 | max accuracy | 0.429148 | 0.940534 | 47.0 |
| 4 | max precision | 0.561962 | 0.625000 | 14.0 |
| 5 | max recall | 0.019804 | 1.000000 | 379.0 |
| 6 | max specificity | 0.860146 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.252175 | 0.377085 | 153.0 |
| 8 | max min_per_class_accuracy | 0.041200 | 0.683761 | 289.0 |
| 9 | max mean_per_class_accuracy | 0.061225 | 0.715869 | 233.0 |
| 10 | max tns | 0.860146 | 7317.000000 | 0.0 |
| 11 | max fns | 0.860146 | 468.000000 | 0.0 |
| 12 | max fps | 0.001557 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019804 | 468.000000 | 379.0 |
| 14 | max tnr | 0.860146 | 0.999863 | 0.0 |
| 15 | max fnr | 0.860146 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001557 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019804 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.02 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.423338 | 8.531668 | 8.531668 | 0.512821 | 0.510171 | 0.512821 | 0.510171 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.390073 | 7.891793 | 8.211730 | 0.474359 | 0.405514 | 0.493590 | 0.457843 | 0.079060 | 0.164530 | 689.179268 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.361291 | 7.038626 | 7.820695 | 0.423077 | 0.373839 | 0.470085 | 0.429841 | 0.070513 | 0.235043 | 603.862590 | 682.069545 | 0.218098 |
| 3 | 4 | 0.040072 | 0.343144 | 5.119001 | 7.145272 | 0.307692 | 0.352418 | 0.429487 | 0.410486 | 0.051282 | 0.286325 | 411.900066 | 614.527175 | 0.262001 |
| 4 | 5 | 0.050090 | 0.320590 | 6.612043 | 7.038626 | 0.397436 | 0.333467 | 0.423077 | 0.395082 | 0.066239 | 0.352564 | 561.204252 | 603.862590 | 0.321818 |
| 5 | 6 | 0.100051 | 0.065884 | 2.993760 | 5.018789 | 0.179949 | 0.162168 | 0.301669 | 0.278775 | 0.149573 | 0.502137 | 199.376002 | 401.878916 | 0.427800 |
| 6 | 7 | 0.150013 | 0.052007 | 0.812592 | 3.617924 | 0.048843 | 0.057204 | 0.217466 | 0.204981 | 0.040598 | 0.542735 | -18.740799 | 261.792384 | 0.417838 |
| 7 | 8 | 0.200103 | 0.047090 | 0.639875 | 2.872456 | 0.038462 | 0.049208 | 0.172657 | 0.165988 | 0.032051 | 0.574786 | -36.012492 | 187.245592 | 0.398645 |
| 8 | 9 | 0.300026 | 0.042381 | 0.812592 | 2.186422 | 0.048843 | 0.044422 | 0.131421 | 0.125501 | 0.081197 | 0.655983 | -18.740799 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.039169 | 0.597983 | 1.789185 | 0.035944 | 0.040680 | 0.107544 | 0.104289 | 0.059829 | 0.715812 | -40.201661 | 78.918522 | 0.335927 |
| 10 | 11 | 0.500000 | 0.036428 | 0.577368 | 1.547009 | 0.034704 | 0.037804 | 0.092987 | 0.091002 | 0.057692 | 0.773504 | -42.263200 | 54.700855 | 0.290995 |
| 11 | 12 | 0.600051 | 0.034188 | 0.768836 | 1.417258 | 0.046213 | 0.035304 | 0.085188 | 0.081715 | 0.076923 | 0.850427 | -23.116421 | 41.725757 | 0.266388 |
| 12 | 13 | 0.699974 | 0.031697 | 0.449064 | 1.279046 | 0.026992 | 0.032899 | 0.076881 | 0.074746 | 0.044872 | 0.895299 | -55.093600 | 27.904571 | 0.207816 |
| 13 | 14 | 0.800026 | 0.029165 | 0.448488 | 1.175176 | 0.026958 | 0.030497 | 0.070637 | 0.069213 | 0.044872 | 0.940171 | -55.151246 | 17.517594 | 0.149108 |
| 14 | 15 | 0.899949 | 0.025335 | 0.363528 | 1.085057 | 0.021851 | 0.027420 | 0.065220 | 0.064572 | 0.036325 | 0.976496 | -63.647200 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.001257 | 0.234922 | 1.000000 | 0.014121 | 0.020494 | 0.060108 | 0.060162 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93269724 | 0.0221618 | 0.96153843 | 0.8730769 | 0.9461538 | 0.9576923 | 0.9269231 | 0.9115385 | 0.9307692 | 0.91923076 | 0.9269231 | 0.9307692 | 0.9423077 | 0.9153846 | 0.9461538 | 0.97692305 | 0.9230769 | 0.95384616 | 0.93822396 | 0.96138996 | 0.8957529 | 0.90733594 | 0.9227799 | 0.969112 | 0.9189189 | 0.93050194 | 0.9150579 | 0.9266409 | 0.93436295 | 0.93050194 | 0.95366794 | 0.93436295 |
| 1 | auc | 0.77155954 | 0.07662893 | 0.8097846 | 0.6586783 | 0.9044715 | 0.6582661 | 0.77823126 | 0.8109185 | 0.88995355 | 0.81813526 | 0.76852864 | 0.8072289 | 0.8346774 | 0.6082885 | 0.7993222 | 0.77954847 | 0.6329946 | 0.78939724 | 0.87335837 | 0.8017511 | 0.68695253 | 0.716318 | 0.80928963 | 0.7125506 | 0.7094298 | 0.8199631 | 0.6626155 | 0.74707115 | 0.8483228 | 0.8274177 | 0.7736996 | 0.809621 |
| 2 | err | 0.06730274 | 0.0221618 | 0.03846154 | 0.12692308 | 0.053846154 | 0.042307694 | 0.073076926 | 0.08846154 | 0.06923077 | 0.08076923 | 0.073076926 | 0.06923077 | 0.057692308 | 0.08461539 | 0.053846154 | 0.023076924 | 0.07692308 | 0.046153847 | 0.06177606 | 0.038610037 | 0.1042471 | 0.09266409 | 0.077220075 | 0.03088803 | 0.08108108 | 0.06949807 | 0.08494209 | 0.07335907 | 0.06563707 | 0.06949807 | 0.046332046 | 0.06563707 |
| 3 | err_count | 17.466667 | 5.751961 | 10.0 | 33.0 | 14.0 | 11.0 | 19.0 | 23.0 | 18.0 | 21.0 | 19.0 | 18.0 | 15.0 | 22.0 | 14.0 | 6.0 | 20.0 | 12.0 | 16.0 | 10.0 | 27.0 | 24.0 | 20.0 | 8.0 | 21.0 | 18.0 | 22.0 | 19.0 | 17.0 | 18.0 | 12.0 | 17.0 |
| 4 | f0point5 | 0.46514317 | 0.12335504 | 0.5555556 | 0.23255815 | 0.51282054 | 0.5 | 0.37974682 | 0.4587156 | 0.40697673 | 0.4 | 0.26666668 | 0.3164557 | 0.390625 | 0.4494382 | 0.58441556 | 0.71428573 | 0.47945204 | 0.64935064 | 0.41095892 | 0.6097561 | 0.26595744 | 0.43103448 | 0.4347826 | 0.6818182 | 0.43956044 | 0.5102041 | 0.32467532 | 0.5208333 | 0.505618 | 0.49107143 | 0.65217394 | 0.37878788 |
| 5 | f1 | 0.45574665 | 0.09789169 | 0.5833333 | 0.26666668 | 0.53333336 | 0.42105263 | 0.38709676 | 0.4651163 | 0.4375 | 0.43243244 | 0.2962963 | 0.35714287 | 0.4 | 0.42105263 | 0.5625 | 0.5 | 0.4117647 | 0.625 | 0.42857143 | 0.5 | 0.27027026 | 0.45454547 | 0.5 | 0.6 | 0.43243244 | 0.5263158 | 0.3125 | 0.51282054 | 0.51428574 | 0.55 | 0.6 | 0.37037036 |
| 6 | f2 | 0.45623723 | 0.0981856 | 0.61403507 | 0.3125 | 0.5555556 | 0.36363637 | 0.39473686 | 0.4716981 | 0.47297296 | 0.47058824 | 0.33333334 | 0.40983605 | 0.40983605 | 0.3960396 | 0.5421687 | 0.3846154 | 0.36082473 | 0.60240966 | 0.4477612 | 0.42372882 | 0.2747253 | 0.48076922 | 0.5882353 | 0.53571427 | 0.42553192 | 0.54347825 | 0.30120483 | 0.5050505 | 0.5232558 | 0.625 | 0.5555556 | 0.36231884 |
| 7 | lift_top_group | 8.954678 | 6.5142984 | 23.636364 | 0.0 | 6.1904764 | 14.444445 | 5.7777777 | 12.380953 | 12.380953 | 10.833333 | 7.878788 | 7.878788 | 14.444445 | 0.0 | 5.098039 | 28.88889 | 4.126984 | 5.098039 | 6.6410255 | 13.282051 | 4.796296 | 0.0 | 11.511111 | 14.388889 | 4.5438595 | 9.592592 | 0.0 | 12.95 | 10.156863 | 5.3958335 | 10.156863 | 6.1666665 |
| 8 | logloss | 0.18464802 | 0.038094055 | 0.11965386 | 0.23314412 | 0.1472362 | 0.16425228 | 0.18327016 | 0.226643 | 0.16274524 | 0.18219237 | 0.16519496 | 0.15293634 | 0.15240738 | 0.25275385 | 0.19253463 | 0.119034655 | 0.25722787 | 0.1737672 | 0.1544161 | 0.15162933 | 0.2394938 | 0.23745379 | 0.17372708 | 0.14156678 | 0.22677377 | 0.1826833 | 0.22541358 | 0.21622324 | 0.17857392 | 0.16693205 | 0.18240076 | 0.1771592 |
| 9 | max_per_class_error | 0.5388279 | 0.10913111 | 0.36363637 | 0.64705884 | 0.42857143 | 0.6666667 | 0.6 | 0.52380955 | 0.5 | 0.5 | 0.6363636 | 0.54545456 | 0.5833333 | 0.61904764 | 0.47058824 | 0.6666667 | 0.6666667 | 0.4117647 | 0.53846157 | 0.61538464 | 0.7222222 | 0.5 | 0.33333334 | 0.5 | 0.57894737 | 0.44444445 | 0.7058824 | 0.5 | 0.47058824 | 0.3125 | 0.47058824 | 0.64285713 |
| 10 | mcc | 0.42809525 | 0.109717056 | 0.5654795 | 0.20925637 | 0.5061643 | 0.41642055 | 0.34849003 | 0.4170607 | 0.404828 | 0.39398798 | 0.26421827 | 0.3308808 | 0.37007472 | 0.3783982 | 0.5351183 | 0.57057095 | 0.3853555 | 0.60184765 | 0.39722785 | 0.5069183 | 0.21427214 | 0.40637684 | 0.47869098 | 0.59763575 | 0.38897252 | 0.48971808 | 0.26801595 | 0.47336474 | 0.4793449 | 0.526398 | 0.5817161 | 0.33605385 |
| 11 | mean_per_class_accuracy | 0.71195424 | 0.054863654 | 0.8061336 | 0.6312031 | 0.7694541 | 0.6606183 | 0.67959183 | 0.71299064 | 0.7276423 | 0.72336066 | 0.6577218 | 0.7031763 | 0.6922043 | 0.6716477 | 0.7523602 | 0.6666667 | 0.65411437 | 0.78382957 | 0.71247655 | 0.6882427 | 0.60984325 | 0.7207113 | 0.8025956 | 0.7459514 | 0.689693 | 0.7570309 | 0.62639767 | 0.73117155 | 0.74611086 | 0.81700104 | 0.7564414 | 0.6622449 |
| 12 | mean_per_class_error | 0.28804573 | 0.054863654 | 0.19386637 | 0.3687969 | 0.23054588 | 0.33938172 | 0.32040817 | 0.28700936 | 0.27235773 | 0.27663934 | 0.3422782 | 0.29682365 | 0.3077957 | 0.32835227 | 0.24763979 | 0.33333334 | 0.34588563 | 0.21617042 | 0.28752345 | 0.31175736 | 0.39015675 | 0.2792887 | 0.19740437 | 0.2540486 | 0.31030703 | 0.24296911 | 0.37360233 | 0.26882845 | 0.25388917 | 0.18299897 | 0.24355859 | 0.3377551 |
| 13 | mse | 0.047694955 | 0.011220353 | 0.027877348 | 0.059998117 | 0.03830212 | 0.038540795 | 0.04757113 | 0.061044708 | 0.04273656 | 0.048351504 | 0.039485093 | 0.038629066 | 0.03859876 | 0.06682954 | 0.05085956 | 0.026970917 | 0.06901286 | 0.044741124 | 0.040211175 | 0.03784169 | 0.062334657 | 0.064471975 | 0.045065224 | 0.033118974 | 0.05985431 | 0.047859993 | 0.05837351 | 0.05696597 | 0.047779147 | 0.044491578 | 0.047194492 | 0.045736782 |
| 14 | null_deviance | 118.01801 | 18.51307 | 92.82863 | 125.73113 | 109.23703 | 98.28862 | 114.7255 | 147.85797 | 109.23703 | 120.223526 | 92.82863 | 92.82863 | 98.28862 | 147.85797 | 125.73113 | 81.936905 | 147.85797 | 125.73113 | 103.63261 | 103.63261 | 131.12575 | 142.19029 | 114.60118 | 98.16257 | 136.65318 | 131.12575 | 125.607956 | 142.19029 | 125.607956 | 120.09978 | 125.607956 | 109.11213 |
| 15 | pr_auc | 0.3348572 | 0.117757134 | 0.56265926 | 0.13233045 | 0.4412647 | 0.29097462 | 0.31898752 | 0.4348375 | 0.35092267 | 0.39832026 | 0.13348988 | 0.25784716 | 0.31036055 | 0.19887343 | 0.35256898 | 0.47871694 | 0.24070711 | 0.4302965 | 0.32692567 | 0.41695774 | 0.13409445 | 0.22896808 | 0.41238353 | 0.42821637 | 0.2719766 | 0.48150206 | 0.13855927 | 0.4147936 | 0.41482958 | 0.33447745 | 0.46910578 | 0.23976788 |
| 16 | precision | 0.4803824 | 0.1665312 | 0.53846157 | 0.21428572 | 0.5 | 0.5714286 | 0.375 | 0.45454547 | 0.3888889 | 0.3809524 | 0.25 | 0.29411766 | 0.3846154 | 0.47058824 | 0.6 | 1.0 | 0.53846157 | 0.6666667 | 0.4 | 0.71428573 | 0.2631579 | 0.41666666 | 0.4 | 0.75 | 0.44444445 | 0.5 | 0.33333334 | 0.5263158 | 0.5 | 0.45833334 | 0.6923077 | 0.3846154 |
| 17 | r2 | 0.15532522 | 0.07934658 | 0.31197202 | 0.018186215 | 0.24819301 | 0.12454372 | 0.12495009 | 0.17779993 | 0.1611523 | 0.16276595 | 0.025486542 | 0.04661377 | 0.123227045 | 0.09988505 | 0.16773029 | 0.19290218 | 0.070478365 | 0.2678528 | 0.15653355 | 0.2062357 | 0.03608319 | 0.095220834 | 0.17403816 | 0.25045416 | 0.11949845 | 0.2599133 | 0.048188265 | 0.20055768 | 0.2209351 | 0.2323715 | 0.23046821 | 0.10551919 |
| 18 | recall | 0.46117207 | 0.10913111 | 0.6363636 | 0.3529412 | 0.5714286 | 0.33333334 | 0.4 | 0.47619048 | 0.5 | 0.5 | 0.36363637 | 0.45454547 | 0.41666666 | 0.3809524 | 0.5294118 | 0.33333334 | 0.33333334 | 0.5882353 | 0.46153846 | 0.3846154 | 0.2777778 | 0.5 | 0.6666667 | 0.5 | 0.42105263 | 0.5555556 | 0.29411766 | 0.5 | 0.5294118 | 0.6875 | 0.5294118 | 0.35714287 |
| 19 | residual_deviance | 95.84001 | 19.760166 | 62.22001 | 121.23495 | 76.56282 | 85.41119 | 95.30048 | 117.854355 | 84.62752 | 94.74003 | 85.901375 | 79.52689 | 79.25184 | 131.432 | 100.118004 | 61.89802 | 133.7585 | 90.35894 | 79.98754 | 78.54399 | 124.057785 | 123.00106 | 89.99062 | 73.3316 | 117.46882 | 94.62995 | 116.76424 | 112.00364 | 92.50129 | 86.4708 | 94.4836 | 91.76847 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:15:14 | 0.000 sec | 2 | .87E1 | 15 | 0.452072 | 0.452172 | 0.452412 | 0.013001 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:15:14 | 0.003 sec | 4 | .54E1 | 15 | 0.450607 | 0.450818 | 0.451011 | 0.012972 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:15:14 | 0.005 sec | 6 | .34E1 | 15 | 0.448304 | 0.448692 | 0.448806 | 0.012928 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:15:14 | 0.008 sec | 8 | .21E1 | 15 | 0.444723 | 0.445388 | 0.445373 | 0.012863 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:15:14 | 0.010 sec | 10 | .13E1 | 15 | 0.439318 | 0.440410 | 0.440179 | 0.012772 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:15:14 | 0.013 sec | 12 | .81E0 | 15 | 0.431515 | 0.433248 | 0.432654 | 0.012659 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:15:14 | 0.015 sec | 14 | .5E0 | 15 | 0.421089 | 0.423728 | 0.422535 | 0.012546 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:15:14 | 0.018 sec | 16 | .31E0 | 15 | 0.408844 | 0.412646 | 0.410540 | 0.012490 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:15:14 | 0.020 sec | 18 | .19E0 | 15 | 0.396783 | 0.401891 | 0.398613 | 0.012553 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:15:14 | 0.023 sec | 20 | .12E0 | 15 | 0.386964 | 0.393342 | 0.388865 | 0.012752 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:15:14 | 0.025 sec | 22 | .74E-1 | 15 | 0.380101 | 0.387598 | 0.382117 | 0.013040 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:15:14 | 0.028 sec | 24 | .46E-1 | 15 | 0.375742 | 0.384180 | 0.377955 | 0.013351 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:15:14 | 0.031 sec | 26 | .29E-1 | 15 | 0.373089 | 0.382311 | 0.375564 | 0.013637 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:15:14 | 0.034 sec | 28 | .18E-1 | 15 | 0.371480 | 0.381359 | 0.374242 | 0.013881 | 0.0 | 28.0 | 0.21873 | 0.185251 | 0.153146 | 0.774425 | 0.29639 | 8.958251 | 0.071796 | 0.222215 | 0.190466 | 0.125735 | 0.763035 | 0.260756 | 8.320513 | 0.082178 | |
| 14 | 2021-07-15 20:15:14 | 0.036 sec | 30 | .11E-1 | 15 | 0.370503 | 0.380931 | 0.373537 | 0.014077 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:15:14 | 0.039 sec | 32 | .69E-2 | 15 | 0.369915 | 0.380795 | 0.374243 | 0.014428 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:15:14 | 0.041 sec | 34 | .43E-2 | 15 | 0.369571 | 0.380813 | 0.376206 | 0.015300 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:15:14 | 0.044 sec | 36 | .27E-2 | 15 | 0.369380 | 0.380900 | 0.379933 | 0.016526 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.548281 | 1.000000 | 0.265854 |
| 1 | Average_Transaction_Frequency | 0.239906 | 0.437560 | 0.116327 |
| 2 | Channel_ID | 0.183724 | 0.335091 | 0.089085 |
| 3 | Minimum_Transaction_Amount | 0.178339 | 0.325269 | 0.086474 |
| 4 | Merchant_ID | 0.177266 | 0.323312 | 0.085954 |
| 5 | Card_Type.1 | 0.155194 | 0.283056 | 0.075251 |
| 6 | Card_Type.0 | 0.153769 | 0.280456 | 0.074560 |
| 7 | Transaction_Amount | 0.100432 | 0.183175 | 0.048698 |
| 8 | Average_Transaction_Amount | 0.076954 | 0.140355 | 0.037314 |
| 9 | Transaction_Date | 0.073129 | 0.133378 | 0.035459 |
| 10 | Day | 0.058541 | 0.106771 | 0.028386 |
| 11 | Month | 0.043640 | 0.079593 | 0.021160 |
| 12 | Maximum_Transaction_Amount | 0.040525 | 0.073912 | 0.019650 |
| 13 | City_ID | 0.032643 | 0.059537 | 0.015828 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201517 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01109 ) | nlambda = 30, lambda.max = 8.7422, lambda.min = 0.01109, lambda.1s... | 14 | 14 | 30 | automl_training_py_691_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.048114512393156464 RMSE: 0.21935020490794274 LogLoss: 0.18614409158881037 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793838 Residual deviance: 2898.635794220955 AIC: 2928.635794220955 AUC: 0.7794190299997898 AUCPR: 0.2876384500110924 Gini: 0.5588380599995797 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2119155579455933:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7000.0 | 318.0 | 0.0435 | (318.0/7318.0) |
| 1 | 1 | 265.0 | 203.0 | 0.5662 | (265.0/468.0) |
| 2 | Total | 7265.0 | 521.0 | 0.0749 | (583.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.211916 | 0.410516 | 162.0 |
| 1 | max f2 | 0.060971 | 0.439315 | 232.0 |
| 2 | max f0point5 | 0.333877 | 0.420040 | 111.0 |
| 3 | max accuracy | 0.547431 | 0.940534 | 10.0 |
| 4 | max precision | 0.843592 | 1.000000 | 0.0 |
| 5 | max recall | 0.020718 | 1.000000 | 377.0 |
| 6 | max specificity | 0.843592 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.211916 | 0.371268 | 162.0 |
| 8 | max min_per_class_accuracy | 0.041711 | 0.700465 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.060971 | 0.712645 | 232.0 |
| 10 | max tns | 0.843592 | 7318.000000 | 0.0 |
| 11 | max fns | 0.843592 | 467.000000 | 0.0 |
| 12 | max fps | 0.002046 | 7318.000000 | 399.0 |
| 13 | max tps | 0.020718 | 468.000000 | 377.0 |
| 14 | max tnr | 0.843592 | 1.000000 | 0.0 |
| 15 | max fnr | 0.843592 | 0.997863 | 0.0 |
| 16 | max fpr | 0.002046 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020718 | 1.000000 | 377.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.419497 | 8.318376 | 8.318376 | 0.500000 | 0.486987 | 0.500000 | 0.486987 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.390679 | 7.038626 | 7.678501 | 0.423077 | 0.404909 | 0.461538 | 0.445948 | 0.070513 | 0.153846 | 603.862590 | 667.850099 | 0.142368 |
| 2 | 3 | 0.030054 | 0.367362 | 7.251918 | 7.536307 | 0.435897 | 0.379033 | 0.452991 | 0.423643 | 0.072650 | 0.226496 | 625.191760 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.350772 | 7.251918 | 7.465209 | 0.435897 | 0.358967 | 0.448718 | 0.407474 | 0.072650 | 0.299145 | 625.191760 | 646.520929 | 0.275642 |
| 4 | 5 | 0.050090 | 0.328879 | 6.185459 | 7.209259 | 0.371795 | 0.340628 | 0.433333 | 0.394105 | 0.061966 | 0.361111 | 518.545913 | 620.925926 | 0.330912 |
| 5 | 6 | 0.100051 | 0.063136 | 2.608848 | 4.912006 | 0.156812 | 0.157140 | 0.295250 | 0.275774 | 0.130342 | 0.491453 | 160.884802 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.050839 | 0.898128 | 3.575192 | 0.053985 | 0.055474 | 0.214897 | 0.202404 | 0.044872 | 0.536325 | -10.187199 | 257.519245 | 0.411017 |
| 7 | 8 | 0.200103 | 0.046792 | 1.066458 | 2.947204 | 0.064103 | 0.048583 | 0.177150 | 0.163899 | 0.053419 | 0.589744 | 6.645847 | 194.720384 | 0.414559 |
| 8 | 9 | 0.300026 | 0.042432 | 0.919512 | 2.271885 | 0.055270 | 0.044303 | 0.136558 | 0.124068 | 0.091880 | 0.681624 | -8.048799 | 127.188524 | 0.406002 |
| 9 | 10 | 0.400077 | 0.039530 | 0.576627 | 1.847935 | 0.034660 | 0.040880 | 0.111075 | 0.103264 | 0.057692 | 0.739316 | -42.337316 | 84.793459 | 0.360934 |
| 10 | 11 | 0.500000 | 0.037071 | 0.727056 | 1.623932 | 0.043702 | 0.038301 | 0.097611 | 0.090282 | 0.072650 | 0.811966 | -27.294399 | 62.393162 | 0.331917 |
| 11 | 12 | 0.600051 | 0.034783 | 0.555270 | 1.445745 | 0.033376 | 0.035916 | 0.086901 | 0.081217 | 0.055556 | 0.867521 | -44.472971 | 44.574516 | 0.284575 |
| 12 | 13 | 0.699974 | 0.032534 | 0.555984 | 1.318730 | 0.033419 | 0.033641 | 0.079266 | 0.074425 | 0.055556 | 0.923077 | -44.401600 | 31.872971 | 0.237370 |
| 13 | 14 | 0.800026 | 0.029736 | 0.277635 | 1.188530 | 0.016688 | 0.031212 | 0.071440 | 0.069021 | 0.027778 | 0.950855 | -72.236486 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.025777 | 0.342144 | 1.094554 | 0.020566 | 0.027908 | 0.065791 | 0.064456 | 0.034188 | 0.985043 | -65.785600 | 9.455441 | 0.090536 |
| 15 | 16 | 1.000000 | 0.001759 | 0.149496 | 1.000000 | 0.008986 | 0.020997 | 0.060108 | 0.060108 | 0.014957 | 1.000000 | -85.050415 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04819608327255456 RMSE: 0.21953606371745524 LogLoss: 0.18717340364651466 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311413 Residual deviance: 728.8532337995281 AIC: 758.8532337995281 AUC: 0.7508687123441222 AUCPR: 0.30131396209377126 Gini: 0.5017374246882444 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.28508545063783586:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1745.0 | 85.0 | 0.0464 | (85.0/1830.0) |
| 1 | 1 | 64.0 | 53.0 | 0.547 | (64.0/117.0) |
| 2 | Total | 1809.0 | 138.0 | 0.0765 | (149.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.285085 | 0.415686 | 110.0 |
| 1 | max f2 | 0.072325 | 0.461538 | 152.0 |
| 2 | max f0point5 | 0.383063 | 0.465465 | 47.0 |
| 3 | max accuracy | 0.391973 | 0.944016 | 43.0 |
| 4 | max precision | 0.499070 | 0.625000 | 6.0 |
| 5 | max recall | 0.020080 | 1.000000 | 378.0 |
| 6 | max specificity | 0.831746 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.285085 | 0.376501 | 110.0 |
| 8 | max min_per_class_accuracy | 0.040718 | 0.666667 | 248.0 |
| 9 | max mean_per_class_accuracy | 0.054208 | 0.728226 | 181.0 |
| 10 | max tns | 0.831746 | 1829.000000 | 0.0 |
| 11 | max fns | 0.831746 | 117.000000 | 0.0 |
| 12 | max fps | 0.002201 | 1830.000000 | 399.0 |
| 13 | max tps | 0.020080 | 117.000000 | 378.0 |
| 14 | max tnr | 0.831746 | 0.999454 | 0.0 |
| 15 | max fnr | 0.831746 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002201 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020080 | 1.000000 | 378.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.40 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.450856 | 9.152564 | 9.152564 | 0.550000 | 0.531796 | 0.550000 | 0.531796 | 0.094017 | 0.094017 | 815.256410 | 815.256410 | 0.089099 |
| 1 | 2 | 0.020031 | 0.401102 | 8.758435 | 8.960552 | 0.526316 | 0.424406 | 0.538462 | 0.479478 | 0.085470 | 0.179487 | 775.843455 | 796.055227 | 0.169651 |
| 2 | 3 | 0.030303 | 0.380238 | 9.152564 | 9.025641 | 0.550000 | 0.390954 | 0.542373 | 0.449470 | 0.094017 | 0.273504 | 815.256410 | 802.564103 | 0.258750 |
| 3 | 4 | 0.040062 | 0.365352 | 4.379217 | 7.893820 | 0.263158 | 0.373849 | 0.474359 | 0.431049 | 0.042735 | 0.316239 | 337.921727 | 689.381986 | 0.293835 |
| 4 | 5 | 0.050334 | 0.344003 | 3.328205 | 6.962062 | 0.200000 | 0.357644 | 0.418367 | 0.416069 | 0.034188 | 0.350427 | 232.820513 | 596.206175 | 0.319280 |
| 5 | 6 | 0.100154 | 0.065381 | 3.259582 | 5.120316 | 0.195876 | 0.210068 | 0.307692 | 0.313596 | 0.162393 | 0.512821 | 225.958234 | 412.031558 | 0.439050 |
| 6 | 7 | 0.149974 | 0.051397 | 1.029342 | 3.761328 | 0.061856 | 0.056944 | 0.226027 | 0.228338 | 0.051282 | 0.564103 | 2.934179 | 276.132771 | 0.440605 |
| 7 | 8 | 0.200308 | 0.046859 | 1.018838 | 3.072189 | 0.061224 | 0.048907 | 0.184615 | 0.183250 | 0.051282 | 0.615385 | 1.883830 | 207.218935 | 0.441614 |
| 8 | 9 | 0.299949 | 0.042405 | 0.343114 | 2.165613 | 0.020619 | 0.044477 | 0.130137 | 0.137151 | 0.034188 | 0.649573 | -65.688607 | 116.561293 | 0.371977 |
| 9 | 10 | 0.400103 | 0.039262 | 0.512032 | 1.751687 | 0.030769 | 0.040794 | 0.105263 | 0.113031 | 0.051282 | 0.700855 | -48.796844 | 75.168691 | 0.319980 |
| 10 | 11 | 0.500257 | 0.036994 | 0.853386 | 1.571842 | 0.051282 | 0.038093 | 0.094456 | 0.098028 | 0.085470 | 0.786325 | -14.661407 | 57.184226 | 0.304358 |
| 11 | 12 | 0.599897 | 0.034754 | 0.343114 | 1.367756 | 0.020619 | 0.035879 | 0.082192 | 0.087705 | 0.034188 | 0.820513 | -65.688607 | 36.775553 | 0.234720 |
| 12 | 13 | 0.700051 | 0.032420 | 0.341354 | 1.220912 | 0.020513 | 0.033594 | 0.073368 | 0.079964 | 0.034188 | 0.854701 | -65.864563 | 22.091164 | 0.164537 |
| 13 | 14 | 0.799692 | 0.029687 | 0.600449 | 1.143603 | 0.036082 | 0.031165 | 0.068722 | 0.073884 | 0.059829 | 0.914530 | -39.955062 | 14.360292 | 0.122180 |
| 14 | 15 | 0.899846 | 0.025870 | 0.341354 | 1.054312 | 0.020513 | 0.027828 | 0.063356 | 0.068757 | 0.034188 | 0.948718 | -65.864563 | 5.431156 | 0.051997 |
| 15 | 16 | 1.000000 | 0.002063 | 0.512032 | 1.000000 | 0.030769 | 0.021352 | 0.060092 | 0.064010 | 0.051282 | 1.000000 | -48.796844 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04829997908807489 RMSE: 0.21977256218207697 LogLoss: 0.18735564289949552 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3542.346216370841 Residual deviance: 2917.5020712309447 AIC: 2947.5020712309447 AUC: 0.7693234747245407 AUCPR: 0.27887076517159737 Gini: 0.5386469494490813 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2539567049179566:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7038.0 | 280.0 | 0.0383 | (280.0/7318.0) |
| 1 | 1 | 275.0 | 193.0 | 0.5876 | (275.0/468.0) |
| 2 | Total | 7313.0 | 473.0 | 0.0713 | (555.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.253957 | 0.410202 | 147.0 |
| 1 | max f2 | 0.062727 | 0.428735 | 224.0 |
| 2 | max f0point5 | 0.335188 | 0.412234 | 107.0 |
| 3 | max accuracy | 0.654935 | 0.940277 | 4.0 |
| 4 | max precision | 0.843432 | 1.000000 | 0.0 |
| 5 | max recall | 0.019411 | 1.000000 | 381.0 |
| 6 | max specificity | 0.843432 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.253957 | 0.372276 | 147.0 |
| 8 | max min_per_class_accuracy | 0.043232 | 0.692676 | 276.0 |
| 9 | max mean_per_class_accuracy | 0.057131 | 0.705074 | 233.0 |
| 10 | max tns | 0.843432 | 7318.000000 | 0.0 |
| 11 | max fns | 0.843432 | 467.000000 | 0.0 |
| 12 | max fps | 0.001688 | 7318.000000 | 399.0 |
| 13 | max tps | 0.019411 | 468.000000 | 381.0 |
| 14 | max tnr | 0.843432 | 1.000000 | 0.0 |
| 15 | max fnr | 0.843432 | 0.997863 | 0.0 |
| 16 | max fpr | 0.001688 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019411 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.05 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.418111 | 8.105084 | 8.105084 | 0.487179 | 0.484658 | 0.487179 | 0.484658 | 0.081197 | 0.081197 | 710.508437 | 710.508437 | 0.075731 |
| 1 | 2 | 0.020036 | 0.386589 | 6.398751 | 7.251918 | 0.384615 | 0.401411 | 0.435897 | 0.443035 | 0.064103 | 0.145299 | 539.875082 | 625.191760 | 0.133274 |
| 2 | 3 | 0.030054 | 0.363725 | 8.318376 | 7.607404 | 0.500000 | 0.374687 | 0.457265 | 0.420252 | 0.083333 | 0.228632 | 731.837607 | 660.740375 | 0.211278 |
| 3 | 4 | 0.040072 | 0.345717 | 7.038626 | 7.465209 | 0.423077 | 0.354925 | 0.448718 | 0.403920 | 0.070513 | 0.299145 | 603.862590 | 646.520929 | 0.275642 |
| 4 | 5 | 0.050090 | 0.320091 | 5.332292 | 7.038626 | 0.320513 | 0.333480 | 0.423077 | 0.389832 | 0.053419 | 0.352564 | 433.229235 | 603.862590 | 0.321818 |
| 5 | 6 | 0.131647 | 0.061121 | 1.807773 | 3.798049 | 0.108661 | 0.108238 | 0.228293 | 0.215381 | 0.147436 | 0.500000 | 80.777307 | 279.804878 | 0.391910 |
| 6 | 7 | 0.169278 | 0.058997 | 1.476299 | 3.281909 | 0.088737 | 0.059107 | 0.197269 | 0.180641 | 0.055556 | 0.555556 | 47.629882 | 228.190862 | 0.410981 |
| 7 | 8 | 0.200103 | 0.051987 | 0.762518 | 2.893812 | 0.045833 | 0.055166 | 0.173941 | 0.161312 | 0.023504 | 0.579060 | -23.748219 | 189.381247 | 0.403192 |
| 8 | 9 | 0.300026 | 0.044341 | 0.940896 | 2.243398 | 0.056555 | 0.047528 | 0.134846 | 0.123416 | 0.094017 | 0.673077 | -5.910399 | 124.339766 | 0.396909 |
| 9 | 10 | 0.400077 | 0.040858 | 0.768836 | 1.874639 | 0.046213 | 0.042509 | 0.112681 | 0.103183 | 0.076923 | 0.750000 | -23.116421 | 87.463884 | 0.372301 |
| 10 | 11 | 0.500000 | 0.038196 | 0.534600 | 1.606838 | 0.032134 | 0.039469 | 0.096584 | 0.090450 | 0.053419 | 0.803419 | -46.540000 | 60.683761 | 0.322823 |
| 11 | 12 | 0.600051 | 0.035582 | 0.555270 | 1.431501 | 0.033376 | 0.036863 | 0.086045 | 0.081515 | 0.055556 | 0.858974 | -44.472971 | 43.150136 | 0.275482 |
| 12 | 13 | 0.699974 | 0.033200 | 0.406296 | 1.285151 | 0.024422 | 0.034394 | 0.077248 | 0.074788 | 0.040598 | 0.899573 | -59.370400 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.030346 | 0.363061 | 1.169834 | 0.021823 | 0.031871 | 0.070316 | 0.069421 | 0.036325 | 0.935897 | -63.693866 | 16.983423 | 0.144561 |
| 14 | 15 | 0.899949 | 0.026383 | 0.384912 | 1.082683 | 0.023136 | 0.028529 | 0.065078 | 0.064881 | 0.038462 | 0.974359 | -61.508800 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.001405 | 0.256279 | 1.000000 | 0.015404 | 0.021479 | 0.060108 | 0.060538 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.92383033 | 0.052398223 | 0.9269231 | 0.9076923 | 0.95 | 0.9346154 | 0.9076923 | 0.93846154 | 0.9307692 | 0.89615387 | 0.9 | 0.9346154 | 0.9461538 | 0.9076923 | 0.93846154 | 0.93846154 | 0.9423077 | 0.93846154 | 0.94208497 | 0.94208497 | 0.93822396 | 0.95366794 | 0.93822396 | 0.8416988 | 0.969112 | 0.9150579 | 0.9459459 | 0.6795367 | 0.96138996 | 0.95366794 | 0.96138996 | 0.93436295 |
| 1 | auc | 0.7766368 | 0.079892136 | 0.7630487 | 0.8357065 | 0.71822804 | 0.80543786 | 0.81496596 | 0.5992 | 0.8303074 | 0.7877921 | 0.7477551 | 0.73959184 | 0.7915215 | 0.75637496 | 0.84366393 | 0.8465306 | 0.82764465 | 0.8673156 | 0.6345308 | 0.8915897 | 0.6618076 | 0.85056585 | 0.6864915 | 0.6101064 | 0.7923695 | 0.8091676 | 0.69736844 | 0.7445219 | 0.77529323 | 0.84110785 | 0.83267456 | 0.89642376 |
| 2 | err | 0.076169685 | 0.052398223 | 0.073076926 | 0.092307694 | 0.05 | 0.06538462 | 0.092307694 | 0.06153846 | 0.06923077 | 0.103846155 | 0.1 | 0.06538462 | 0.053846154 | 0.092307694 | 0.06153846 | 0.06153846 | 0.057692308 | 0.06153846 | 0.057915058 | 0.057915058 | 0.06177606 | 0.046332046 | 0.06177606 | 0.15830116 | 0.03088803 | 0.08494209 | 0.054054055 | 0.32046333 | 0.038610037 | 0.046332046 | 0.038610037 | 0.06563707 |
| 3 | err_count | 19.766666 | 13.57148 | 19.0 | 24.0 | 13.0 | 17.0 | 24.0 | 16.0 | 18.0 | 27.0 | 26.0 | 17.0 | 14.0 | 24.0 | 16.0 | 16.0 | 15.0 | 16.0 | 15.0 | 15.0 | 16.0 | 12.0 | 16.0 | 41.0 | 8.0 | 22.0 | 14.0 | 83.0 | 10.0 | 12.0 | 10.0 | 17.0 |
| 4 | f0point5 | 0.44548142 | 0.13570912 | 0.4945055 | 0.24271844 | 0.6164383 | 0.65 | 0.3271028 | 0.25862068 | 0.47058824 | 0.516129 | 0.34351146 | 0.443038 | 0.5 | 0.47826087 | 0.56122446 | 0.48192772 | 0.5479452 | 0.5 | 0.33898306 | 0.5555556 | 0.32608697 | 0.625 | 0.51724136 | 0.23648648 | 0.5882353 | 0.5 | 0.3846154 | 0.09615385 | 0.51282054 | 0.5714286 | 0.2631579 | 0.41666666 |
| 5 | f1 | 0.4423002 | 0.11687098 | 0.4864865 | 0.29411766 | 0.58064514 | 0.60465115 | 0.36842105 | 0.27272728 | 0.47058824 | 0.5423729 | 0.4090909 | 0.4516129 | 0.5 | 0.47826087 | 0.57894737 | 0.5 | 0.516129 | 0.5 | 0.3478261 | 0.54545456 | 0.27272728 | 0.5714286 | 0.42857143 | 0.25454545 | 0.5 | 0.5 | 0.36363637 | 0.14432989 | 0.44444445 | 0.5714286 | 0.2857143 | 0.4848485 |
| 6 | f2 | 0.44957304 | 0.10308646 | 0.4787234 | 0.37313432 | 0.5487805 | 0.5652174 | 0.42168674 | 0.28846154 | 0.47058824 | 0.5714286 | 0.505618 | 0.46052632 | 0.5 | 0.47826087 | 0.59782606 | 0.5194805 | 0.4878049 | 0.5 | 0.35714287 | 0.53571427 | 0.234375 | 0.5263158 | 0.36585367 | 0.27559054 | 0.4347826 | 0.5 | 0.3448276 | 0.2892562 | 0.39215687 | 0.5714286 | 0.3125 | 0.5797101 |
| 7 | lift_top_group | 7.3459773 | 5.217934 | 4.5614033 | 0.0 | 10.196078 | 10.833333 | 5.7777777 | 0.0 | 10.196078 | 6.419753 | 0.0 | 11.555555 | 12.380953 | 7.536232 | 4.814815 | 11.555555 | 10.196078 | 5.4166665 | 0.0 | 15.235294 | 6.1666665 | 16.1875 | 4.796296 | 3.5972223 | 17.266666 | 3.9242425 | 7.1944447 | 0.0 | 7.848485 | 12.333333 | 14.388889 | 0.0 |
| 8 | logloss | 0.18553685 | 0.04338414 | 0.21209325 | 0.16156504 | 0.18721515 | 0.23383115 | 0.19075131 | 0.1592548 | 0.18806219 | 0.27306208 | 0.19382353 | 0.18195133 | 0.1593117 | 0.24849382 | 0.18038824 | 0.16805322 | 0.1812787 | 0.172908 | 0.16095363 | 0.17322893 | 0.20151673 | 0.16758667 | 0.21756634 | 0.30960086 | 0.12652567 | 0.25105715 | 0.17379239 | 0.14416713 | 0.14410287 | 0.14802289 | 0.117651135 | 0.13828951 |
| 9 | max_per_class_error | 0.5312282 | 0.113243386 | 0.5263158 | 0.54545456 | 0.47058824 | 0.45833334 | 0.53333336 | 0.7 | 0.5294118 | 0.4074074 | 0.4 | 0.53333336 | 0.5 | 0.5217391 | 0.3888889 | 0.46666667 | 0.5294118 | 0.5 | 0.6363636 | 0.47058824 | 0.78571427 | 0.5 | 0.6666667 | 0.7083333 | 0.6 | 0.5 | 0.6666667 | 0.32669324 | 0.6363636 | 0.42857143 | 0.6666667 | 0.33333334 |
| 10 | mcc | 0.41527593 | 0.11322856 | 0.44736934 | 0.27096462 | 0.5572801 | 0.5741567 | 0.32955298 | 0.24196774 | 0.4335512 | 0.48650235 | 0.3839382 | 0.417132 | 0.4715447 | 0.42762795 | 0.5467332 | 0.46838892 | 0.48834917 | 0.46721312 | 0.31791216 | 0.5148267 | 0.2533919 | 0.5538034 | 0.41806352 | 0.16931283 | 0.5020122 | 0.4535865 | 0.33717602 | 0.19974963 | 0.43716675 | 0.5469388 | 0.26920313 | 0.47286174 |
| 11 | mean_per_class_accuracy | 0.71397 | 0.05351191 | 0.7181699 | 0.69112813 | 0.75441784 | 0.7581215 | 0.70068026 | 0.632 | 0.7167756 | 0.7619615 | 0.75918365 | 0.714966 | 0.7357724 | 0.71381396 | 0.78696054 | 0.7482993 | 0.72294843 | 0.7336066 | 0.6656892 | 0.75024307 | 0.5969388 | 0.74176955 | 0.65836793 | 0.5947695 | 0.69598395 | 0.7267932 | 0.6545209 | 0.7741534 | 0.6757698 | 0.7734694 | 0.65480894 | 0.80701756 |
| 12 | mean_per_class_error | 0.28602996 | 0.05351191 | 0.2818301 | 0.30887187 | 0.24558218 | 0.24187852 | 0.2993197 | 0.368 | 0.2832244 | 0.23803847 | 0.24081632 | 0.285034 | 0.26422763 | 0.286186 | 0.21303949 | 0.25170067 | 0.27705157 | 0.26639345 | 0.33431086 | 0.24975693 | 0.4030612 | 0.25823045 | 0.3416321 | 0.4052305 | 0.30401605 | 0.27320674 | 0.34547907 | 0.22584662 | 0.3242302 | 0.22653061 | 0.34519103 | 0.19298245 |
| 13 | mse | 0.04791499 | 0.013328202 | 0.056754097 | 0.041609783 | 0.04821344 | 0.06319375 | 0.05107851 | 0.03651143 | 0.04952743 | 0.07614301 | 0.05041871 | 0.04615964 | 0.04004746 | 0.06689089 | 0.049076565 | 0.043528136 | 0.047544423 | 0.04500068 | 0.038349524 | 0.046297073 | 0.04976875 | 0.043077517 | 0.05579358 | 0.08350024 | 0.029890012 | 0.069727845 | 0.041883945 | 0.030910734 | 0.035077237 | 0.03808622 | 0.0273951 | 0.035993893 |
| 14 | null_deviance | 118.07821 | 27.262102 | 136.7752 | 92.82863 | 125.73113 | 164.55519 | 114.7255 | 87.37807 | 125.73113 | 181.34087 | 114.7255 | 114.7255 | 109.23703 | 158.97968 | 131.24835 | 114.7255 | 125.73113 | 120.223526 | 92.701996 | 125.607956 | 109.11213 | 120.09978 | 131.12575 | 164.43605 | 87.25086 | 153.29364 | 98.16257 | 76.37677 | 92.701996 | 109.11213 | 65.54008 | 98.16257 |
| 15 | pr_auc | 0.3001417 | 0.13219908 | 0.28805044 | 0.14331585 | 0.35414135 | 0.50765073 | 0.20109563 | 0.09307386 | 0.4082734 | 0.41547522 | 0.2086935 | 0.32021332 | 0.40612248 | 0.41412452 | 0.35724205 | 0.33495218 | 0.391467 | 0.39826396 | 0.12643215 | 0.5016468 | 0.14359426 | 0.52689046 | 0.27660674 | 0.15980321 | 0.4001534 | 0.3002186 | 0.15971841 | 0.06064692 | 0.30818498 | 0.40564275 | 0.10629552 | 0.28626105 |
| 16 | precision | 0.45233172 | 0.15301102 | 0.5 | 0.2173913 | 0.64285713 | 0.68421054 | 0.3043478 | 0.25 | 0.47058824 | 0.5 | 0.31034482 | 0.4375 | 0.5 | 0.47826087 | 0.55 | 0.47058824 | 0.5714286 | 0.5 | 0.33333334 | 0.5625 | 0.375 | 0.6666667 | 0.6 | 0.22580644 | 0.6666667 | 0.5 | 0.4 | 0.07865169 | 0.5714286 | 0.5714286 | 0.25 | 0.3809524 |
| 17 | r2 | 0.13129963 | 0.10967983 | 0.16213651 | -0.026951946 | 0.21103159 | 0.24578077 | 0.060433384 | 0.012731007 | 0.18952933 | 0.18180458 | 0.07257016 | 0.15091382 | 0.21393485 | 0.1704597 | 0.23838942 | 0.19931918 | 0.2219794 | 0.22078744 | 0.056992553 | 0.24510111 | 0.02666543 | 0.2567688 | 0.13723166 | 0.0068652565 | 0.19475828 | 0.102912635 | 0.052086014 | -0.032630935 | 0.1374574 | 0.25514236 | -0.21059991 | 0.1853892 |
| 18 | recall | 0.47549492 | 0.13048036 | 0.47368422 | 0.45454547 | 0.5294118 | 0.5416667 | 0.46666667 | 0.3 | 0.47058824 | 0.5925926 | 0.6 | 0.46666667 | 0.5 | 0.47826087 | 0.6111111 | 0.53333336 | 0.47058824 | 0.5 | 0.36363637 | 0.5294118 | 0.21428572 | 0.5 | 0.33333334 | 0.29166666 | 0.4 | 0.5 | 0.33333334 | 0.875 | 0.36363637 | 0.5714286 | 0.33333334 | 0.6666667 |
| 19 | residual_deviance | 96.314224 | 22.537617 | 110.28849 | 84.01382 | 97.35188 | 121.59219 | 99.19068 | 82.8125 | 97.792336 | 141.99228 | 100.78823 | 94.61469 | 82.84209 | 129.2168 | 93.80189 | 87.38767 | 94.26493 | 89.912155 | 83.37398 | 89.73259 | 104.385666 | 86.80989 | 112.69936 | 160.37325 | 65.5403 | 130.0476 | 90.02445 | 74.67857 | 74.64529 | 76.67586 | 60.943287 | 71.633965 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:15:27 | 0.000 sec | 2 | .87E1 | 15.0 | 0.452144 | 0.451979 | 0.452627 | 0.019003 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:15:27 | 0.003 sec | 4 | .54E1 | 15.0 | 0.450722 | 0.45051 | 0.451269 | 0.018927 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:15:27 | 0.006 sec | 6 | .34E1 | 15.0 | 0.448485 | 0.448203 | 0.449132 | 0.018808 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:15:27 | 0.010 sec | 8 | .21E1 | 15.0 | 0.445003 | 0.444616 | 0.445802 | 0.018625 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:15:27 | 0.013 sec | 10 | .13E1 | 15.0 | 0.439744 | 0.439211 | 0.440759 | 0.018353 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:15:27 | 0.016 sec | 12 | .81E0 | 15.0 | 0.432141 | 0.431429 | 0.433436 | 0.017969 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:15:27 | 0.019 sec | 14 | .5E0 | 15.0 | 0.421958 | 0.421076 | 0.423562 | 0.017476 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:15:27 | 0.022 sec | 16 | .31E0 | 15.0 | 0.409958 | 0.409017 | 0.411805 | 0.016938 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:15:27 | 0.025 sec | 18 | .19E0 | 15.0 | 0.398093 | 0.39732 | 0.400039 | 0.01647 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:15:27 | 0.029 sec | 20 | .12E0 | 15.0 | 0.388397 | 0.388057 | 0.390355 | 0.016168 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:15:27 | 0.055 sec | 22 | .75E-1 | 15.0 | 0.381607 | 0.381873 | 0.383599 | 0.016035 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:15:27 | 0.117 sec | 24 | .46E-1 | 15.0 | 0.377305 | 0.378207 | 0.379408 | 0.016016 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:15:27 | 0.183 sec | 26 | .29E-1 | 15.0 | 0.374716 | 0.376163 | 0.377001 | 0.016055 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:15:27 | 0.222 sec | 28 | .18E-1 | 15.0 | 0.373184 | 0.37502 | 0.37569 | 0.016114 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:15:27 | 1.969 sec | 29 | None | NaN | 29.0 | 0.21935 | 0.186144 | 0.148339 | 0.779419 | 0.287638 | 8.318376 | 0.074878 | 0.219536 | 0.187173 | 0.14669 | 0.750869 | 0.301314 | 9.152564 | 0.076528 | ||||||
| 15 | 2021-07-15 20:15:27 | 0.264 sec | 30 | .11E-1 | 15.0 | 0.372288 | 0.374347 | 0.374682 | 0.016053 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:15:27 | 0.307 sec | 32 | .69E-2 | 15.0 | 0.371773 | 0.373917 | 0.375018 | 0.016048 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:15:27 | 0.349 sec | 34 | .43E-2 | 15.0 | 0.371482 | 0.373631 | 0.375518 | 0.016047 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:15:27 | 0.379 sec | 35 | .27E-2 | 15.0 | 0.371324 | 0.373445 | 0.375404 | 0.016056 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.557426 | 1.000000 | 0.293922 |
| 1 | Average_Transaction_Frequency | 0.226365 | 0.406089 | 0.119359 |
| 2 | Merchant_ID | 0.203007 | 0.364186 | 0.107042 |
| 3 | Minimum_Transaction_Amount | 0.174708 | 0.313419 | 0.092121 |
| 4 | Channel_ID | 0.165194 | 0.296352 | 0.087104 |
| 5 | Card_Type.1 | 0.126651 | 0.227207 | 0.066781 |
| 6 | Card_Type.0 | 0.125243 | 0.224681 | 0.066039 |
| 7 | Transaction_Amount | 0.115818 | 0.207773 | 0.061069 |
| 8 | Transaction_Date | 0.067745 | 0.121533 | 0.035721 |
| 9 | Average_Transaction_Amount | 0.043962 | 0.078866 | 0.023180 |
| 10 | Day | 0.039993 | 0.071745 | 0.021087 |
| 11 | Month | 0.031802 | 0.057051 | 0.016769 |
| 12 | Maximum_Transaction_Amount | 0.012688 | 0.022762 | 0.006690 |
| 13 | City_ID | 0.005910 | 0.010602 | 0.003116 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201533 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01111 ) | nlambda = 30, lambda.max = 8.7588, lambda.min = 0.01111, lambda.1s... | 14 | 14 | 30 | automl_training_py_723_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.047506448107440466 RMSE: 0.21795973964803791 LogLoss: 0.18319084235979283 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938348 Residual deviance: 2852.647797226694 AIC: 2882.647797226694 AUC: 0.7916745502834599 AUCPR: 0.31069016787186937 Gini: 0.5833491005669198 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.1630425946817641:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6973.0 | 345.0 | 0.0471 | (345.0/7318.0) |
| 1 | 1 | 253.0 | 215.0 | 0.5406 | (253.0/468.0) |
| 2 | Total | 7226.0 | 560.0 | 0.0768 | (598.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.163043 | 0.418288 | 177.0 |
| 1 | max f2 | 0.072316 | 0.453502 | 218.0 |
| 2 | max f0point5 | 0.349933 | 0.430825 | 97.0 |
| 3 | max accuracy | 0.430422 | 0.941433 | 47.0 |
| 4 | max precision | 0.805647 | 0.750000 | 3.0 |
| 5 | max recall | 0.016254 | 1.000000 | 381.0 |
| 6 | max specificity | 0.865674 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.163043 | 0.379267 | 177.0 |
| 8 | max min_per_class_accuracy | 0.041937 | 0.713675 | 283.0 |
| 9 | max mean_per_class_accuracy | 0.050404 | 0.723609 | 255.0 |
| 10 | max tns | 0.865674 | 7317.000000 | 0.0 |
| 11 | max fns | 0.865674 | 468.000000 | 0.0 |
| 12 | max fps | 0.000670 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016254 | 468.000000 | 381.0 |
| 14 | max tnr | 0.865674 | 0.999863 | 0.0 |
| 15 | max fnr | 0.865674 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000670 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016254 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.425700 | 9.384835 | 9.384835 | 0.564103 | 0.513849 | 0.564103 | 0.513849 | 0.094017 | 0.094017 | 838.483454 | 838.483454 | 0.089371 |
| 1 | 2 | 0.020036 | 0.391264 | 7.891793 | 8.638314 | 0.474359 | 0.406433 | 0.519231 | 0.460141 | 0.079060 | 0.173077 | 689.179268 | 763.831361 | 0.162828 |
| 2 | 3 | 0.030054 | 0.364195 | 7.465209 | 8.247279 | 0.448718 | 0.376242 | 0.495726 | 0.432175 | 0.074786 | 0.247863 | 646.520929 | 724.727884 | 0.231739 |
| 3 | 4 | 0.040072 | 0.345520 | 5.758876 | 7.625178 | 0.346154 | 0.354686 | 0.458333 | 0.412802 | 0.057692 | 0.305556 | 475.887574 | 662.517806 | 0.282462 |
| 4 | 5 | 0.050090 | 0.323764 | 5.119001 | 7.123943 | 0.307692 | 0.335347 | 0.428205 | 0.397311 | 0.051282 | 0.356838 | 411.900066 | 612.394258 | 0.326365 |
| 5 | 6 | 0.100051 | 0.068072 | 3.036528 | 5.082859 | 0.182519 | 0.170723 | 0.305520 | 0.284163 | 0.151709 | 0.508547 | 203.652802 | 408.285880 | 0.434620 |
| 6 | 7 | 0.150013 | 0.052415 | 1.026432 | 3.731874 | 0.061697 | 0.058582 | 0.224315 | 0.209033 | 0.051282 | 0.559829 | 2.643201 | 273.187420 | 0.436025 |
| 7 | 8 | 0.200103 | 0.047298 | 1.151775 | 3.086021 | 0.069231 | 0.049614 | 0.185494 | 0.169127 | 0.057692 | 0.617521 | 15.177515 | 208.602142 | 0.444113 |
| 8 | 9 | 0.300026 | 0.042074 | 0.898128 | 2.357348 | 0.053985 | 0.044405 | 0.141695 | 0.127589 | 0.089744 | 0.707265 | -10.187199 | 135.734801 | 0.433283 |
| 9 | 10 | 0.400077 | 0.038700 | 0.448488 | 1.879980 | 0.026958 | 0.040357 | 0.113002 | 0.105774 | 0.044872 | 0.752137 | -55.151246 | 87.997970 | 0.374575 |
| 10 | 11 | 0.500000 | 0.036162 | 0.748440 | 1.653846 | 0.044987 | 0.037391 | 0.099409 | 0.092108 | 0.074786 | 0.826923 | -25.155999 | 65.384615 | 0.347830 |
| 11 | 12 | 0.600051 | 0.033581 | 0.662053 | 1.488477 | 0.039795 | 0.034846 | 0.089469 | 0.082560 | 0.066239 | 0.893162 | -33.794696 | 48.847654 | 0.311856 |
| 12 | 13 | 0.699974 | 0.031045 | 0.299376 | 1.318730 | 0.017995 | 0.032346 | 0.079266 | 0.075392 | 0.029915 | 0.923077 | -70.062400 | 31.872971 | 0.237370 |
| 13 | 14 | 0.800026 | 0.027926 | 0.320348 | 1.193872 | 0.019255 | 0.029566 | 0.071761 | 0.069661 | 0.032051 | 0.955128 | -67.965176 | 19.387192 | 0.165022 |
| 14 | 15 | 0.899949 | 0.023457 | 0.256608 | 1.089806 | 0.015424 | 0.025829 | 0.065506 | 0.064794 | 0.025641 | 0.980769 | -74.339200 | 8.980580 | 0.085989 |
| 15 | 16 | 1.000000 | 0.000481 | 0.192209 | 1.000000 | 0.011553 | 0.017957 | 0.060108 | 0.060108 | 0.019231 | 1.000000 | -80.779105 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.05074244991680071 RMSE: 0.22526084861067336 LogLoss: 0.19789967652271856 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311421 Residual deviance: 770.6213403794659 AIC: 800.6213403794659 AUC: 0.7048082761197515 AUCPR: 0.20911247044405312 Gini: 0.409616552239503 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.29338227516169246:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1762.0 | 68.0 | 0.0372 | (68.0/1830.0) |
| 1 | 1 | 74.0 | 43.0 | 0.6325 | (74.0/117.0) |
| 2 | Total | 1836.0 | 111.0 | 0.0729 | (142.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.293382 | 0.377193 | 87.0 |
| 1 | max f2 | 0.097656 | 0.395161 | 126.0 |
| 2 | max f0point5 | 0.333519 | 0.408389 | 67.0 |
| 3 | max accuracy | 0.701380 | 0.939908 | 1.0 |
| 4 | max precision | 0.701380 | 0.500000 | 1.0 |
| 5 | max recall | 0.017633 | 1.000000 | 371.0 |
| 6 | max specificity | 0.837674 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.333519 | 0.339862 | 67.0 |
| 8 | max min_per_class_accuracy | 0.039776 | 0.632787 | 248.0 |
| 9 | max mean_per_class_accuracy | 0.067717 | 0.683971 | 151.0 |
| 10 | max tns | 0.837674 | 1829.000000 | 0.0 |
| 11 | max fns | 0.837674 | 117.000000 | 0.0 |
| 12 | max fps | 0.000583 | 1830.000000 | 399.0 |
| 13 | max tps | 0.017633 | 117.000000 | 371.0 |
| 14 | max tnr | 0.837674 | 0.999454 | 0.0 |
| 15 | max fnr | 0.837674 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000583 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017633 | 1.000000 | 371.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.97 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.428046 | 4.160256 | 4.160256 | 0.250000 | 0.512641 | 0.250000 | 0.512641 | 0.042735 | 0.042735 | 316.025641 | 316.025641 | 0.034538 |
| 1 | 2 | 0.020031 | 0.393781 | 6.130904 | 5.120316 | 0.368421 | 0.409028 | 0.307692 | 0.462163 | 0.059829 | 0.102564 | 513.090418 | 412.031558 | 0.087810 |
| 2 | 3 | 0.030303 | 0.363044 | 8.320513 | 6.205128 | 0.500000 | 0.378360 | 0.372881 | 0.433755 | 0.085470 | 0.188034 | 732.051282 | 520.512821 | 0.167816 |
| 3 | 4 | 0.040062 | 0.336776 | 8.758435 | 6.827087 | 0.526316 | 0.353219 | 0.410256 | 0.414137 | 0.085470 | 0.273504 | 775.843455 | 582.708744 | 0.248368 |
| 4 | 5 | 0.050334 | 0.314882 | 5.824359 | 6.622449 | 0.350000 | 0.326758 | 0.397959 | 0.396305 | 0.059829 | 0.333333 | 482.435897 | 562.244898 | 0.301093 |
| 5 | 6 | 0.100154 | 0.066498 | 2.230241 | 4.437607 | 0.134021 | 0.157640 | 0.266667 | 0.277584 | 0.111111 | 0.444444 | 123.024055 | 343.760684 | 0.366302 |
| 6 | 7 | 0.149974 | 0.052445 | 0.514671 | 3.134440 | 0.030928 | 0.058385 | 0.188356 | 0.204768 | 0.025641 | 0.470085 | -48.532910 | 213.443976 | 0.340577 |
| 7 | 8 | 0.200308 | 0.047761 | 0.339613 | 2.432150 | 0.020408 | 0.049773 | 0.146154 | 0.165821 | 0.017094 | 0.487179 | -66.038723 | 143.214990 | 0.305212 |
| 8 | 9 | 0.299949 | 0.042400 | 0.772006 | 1.880664 | 0.046392 | 0.044786 | 0.113014 | 0.125614 | 0.076923 | 0.564103 | -22.799366 | 88.066386 | 0.281042 |
| 9 | 10 | 0.400103 | 0.039135 | 0.853386 | 1.623515 | 0.051282 | 0.040735 | 0.097561 | 0.104367 | 0.085470 | 0.649573 | -14.661407 | 62.351470 | 0.265420 |
| 10 | 11 | 0.500257 | 0.036472 | 0.682709 | 1.435160 | 0.041026 | 0.037712 | 0.086242 | 0.091022 | 0.068376 | 0.717949 | -31.729126 | 43.516032 | 0.231610 |
| 11 | 12 | 0.599897 | 0.033919 | 0.600449 | 1.296518 | 0.036082 | 0.035227 | 0.077911 | 0.081755 | 0.059829 | 0.777778 | -39.955062 | 29.651826 | 0.189253 |
| 12 | 13 | 0.700051 | 0.031269 | 0.682709 | 1.208703 | 0.041026 | 0.032554 | 0.072634 | 0.074716 | 0.068376 | 0.846154 | -31.729126 | 20.870252 | 0.155443 |
| 13 | 14 | 0.799692 | 0.028130 | 0.514671 | 1.122227 | 0.030928 | 0.029787 | 0.067437 | 0.069118 | 0.051282 | 0.897436 | -48.532910 | 12.222716 | 0.103993 |
| 14 | 15 | 0.899846 | 0.023673 | 0.597370 | 1.063810 | 0.035897 | 0.026056 | 0.063927 | 0.064325 | 0.059829 | 0.957265 | -40.262985 | 6.380986 | 0.061090 |
| 15 | 16 | 1.000000 | 0.000499 | 0.426693 | 1.000000 | 0.025641 | 0.018255 | 0.060092 | 0.059711 | 0.042735 | 1.000000 | -57.330703 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.047819970247988175 RMSE: 0.21867777721567452 LogLoss: 0.18455561170487209 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.3848221936078 Residual deviance: 2873.899985468268 AIC: 2903.899985468268 AUC: 0.7804615653242327 AUCPR: 0.29710013350164993 Gini: 0.5609231306484654 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.12207285458201035:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6956.0 | 362.0 | 0.0495 | (362.0/7318.0) |
| 1 | 1 | 249.0 | 219.0 | 0.5321 | (249.0/468.0) |
| 2 | Total | 7205.0 | 581.0 | 0.0785 | (611.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.122073 | 0.417541 | 185.0 |
| 1 | max f2 | 0.070979 | 0.453679 | 220.0 |
| 2 | max f0point5 | 0.311526 | 0.410517 | 119.0 |
| 3 | max accuracy | 0.445991 | 0.940920 | 36.0 |
| 4 | max precision | 0.820140 | 0.666667 | 2.0 |
| 5 | max recall | 0.014788 | 1.000000 | 385.0 |
| 6 | max specificity | 0.889794 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.122073 | 0.378521 | 185.0 |
| 8 | max min_per_class_accuracy | 0.041563 | 0.700855 | 282.0 |
| 9 | max mean_per_class_accuracy | 0.070979 | 0.719223 | 220.0 |
| 10 | max tns | 0.889794 | 7317.000000 | 0.0 |
| 11 | max fns | 0.889794 | 468.000000 | 0.0 |
| 12 | max fps | 0.000686 | 7318.000000 | 399.0 |
| 13 | max tps | 0.014788 | 468.000000 | 385.0 |
| 14 | max tnr | 0.889794 | 0.999863 | 0.0 |
| 15 | max fnr | 0.889794 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000686 | 1.000000 | 399.0 |
| 17 | max tpr | 0.014788 | 1.000000 | 385.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.428965 | 9.171543 | 9.171543 | 0.551282 | 0.516183 | 0.551282 | 0.516183 | 0.091880 | 0.091880 | 817.154284 | 817.154284 | 0.087098 |
| 1 | 2 | 0.020036 | 0.387739 | 7.891793 | 8.531668 | 0.474359 | 0.406146 | 0.512821 | 0.461165 | 0.079060 | 0.170940 | 689.179268 | 753.166776 | 0.160555 |
| 2 | 3 | 0.030054 | 0.361049 | 6.825334 | 7.962890 | 0.410256 | 0.374165 | 0.478632 | 0.432165 | 0.068376 | 0.239316 | 582.533421 | 696.288991 | 0.222645 |
| 3 | 4 | 0.040072 | 0.343924 | 5.119001 | 7.251918 | 0.307692 | 0.352950 | 0.435897 | 0.412361 | 0.051282 | 0.290598 | 411.900066 | 625.191760 | 0.266548 |
| 4 | 5 | 0.050090 | 0.323949 | 5.972167 | 6.995968 | 0.358974 | 0.334965 | 0.420513 | 0.396882 | 0.059829 | 0.350427 | 497.216743 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.068000 | 3.164832 | 5.082859 | 0.190231 | 0.170705 | 0.305520 | 0.283938 | 0.158120 | 0.508547 | 216.483203 | 408.285880 | 0.434620 |
| 6 | 7 | 0.150013 | 0.052466 | 0.983664 | 3.717630 | 0.059126 | 0.058508 | 0.223459 | 0.208859 | 0.049145 | 0.557692 | -1.633599 | 271.763040 | 0.433751 |
| 7 | 8 | 0.200103 | 0.047495 | 1.023800 | 3.043308 | 0.061538 | 0.049657 | 0.182927 | 0.169008 | 0.051282 | 0.608974 | 2.380013 | 204.330832 | 0.435020 |
| 8 | 9 | 0.300026 | 0.042087 | 0.769824 | 2.286129 | 0.046272 | 0.044470 | 0.137414 | 0.127531 | 0.076923 | 0.685897 | -23.017599 | 128.612904 | 0.410549 |
| 9 | 10 | 0.400077 | 0.038801 | 0.683410 | 1.885321 | 0.041078 | 0.040391 | 0.113323 | 0.105739 | 0.068376 | 0.754274 | -31.659041 | 88.532055 | 0.376848 |
| 10 | 11 | 0.500000 | 0.036133 | 0.534600 | 1.615385 | 0.032134 | 0.037446 | 0.097097 | 0.092091 | 0.053419 | 0.807692 | -46.540000 | 61.538462 | 0.327370 |
| 11 | 12 | 0.600051 | 0.033640 | 0.683410 | 1.459989 | 0.041078 | 0.034909 | 0.087757 | 0.082556 | 0.068376 | 0.876068 | -31.659041 | 45.998895 | 0.293669 |
| 12 | 13 | 0.699974 | 0.031209 | 0.320760 | 1.297361 | 0.019280 | 0.032422 | 0.077982 | 0.075400 | 0.032051 | 0.908120 | -67.924000 | 29.736141 | 0.221457 |
| 13 | 14 | 0.800026 | 0.028064 | 0.427131 | 1.188530 | 0.025674 | 0.029637 | 0.071440 | 0.069676 | 0.042735 | 0.950855 | -57.286901 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.023549 | 0.277992 | 1.087431 | 0.016710 | 0.025927 | 0.065363 | 0.064819 | 0.027778 | 0.978632 | -72.200800 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000463 | 0.213565 | 1.000000 | 0.012837 | 0.018089 | 0.060108 | 0.060144 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9314261 | 0.027794126 | 0.9230769 | 0.9307692 | 0.9269231 | 0.9653846 | 0.91923076 | 0.9230769 | 0.9 | 0.88076925 | 0.9692308 | 0.9269231 | 0.9653846 | 0.9230769 | 0.8384615 | 0.93846154 | 0.9269231 | 0.96153843 | 0.90733594 | 0.93822396 | 0.969112 | 0.95752895 | 0.95366794 | 0.9498069 | 0.9459459 | 0.9266409 | 0.9266409 | 0.9227799 | 0.9189189 | 0.96138996 | 0.9227799 | 0.9227799 |
| 1 | auc | 0.77668464 | 0.07544048 | 0.8395722 | 0.78557825 | 0.8035374 | 0.8247012 | 0.727459 | 0.79632515 | 0.7506383 | 0.80175334 | 0.6314484 | 0.78123134 | 0.69146824 | 0.89318645 | 0.75147927 | 0.8157823 | 0.6929461 | 0.78560454 | 0.7404334 | 0.78267545 | 0.8478407 | 0.9241255 | 0.8755144 | 0.7985247 | 0.74136543 | 0.7147541 | 0.8675255 | 0.7739035 | 0.70394737 | 0.57871485 | 0.8446064 | 0.7338962 |
| 2 | err | 0.06857391 | 0.027794126 | 0.07692308 | 0.06923077 | 0.073076926 | 0.034615386 | 0.08076923 | 0.07692308 | 0.1 | 0.11923077 | 0.03076923 | 0.073076926 | 0.034615386 | 0.07692308 | 0.16153847 | 0.06153846 | 0.073076926 | 0.03846154 | 0.09266409 | 0.06177606 | 0.03088803 | 0.042471044 | 0.046332046 | 0.05019305 | 0.054054055 | 0.07335907 | 0.07335907 | 0.077220075 | 0.08108108 | 0.038610037 | 0.077220075 | 0.077220075 |
| 3 | err_count | 17.8 | 7.2273426 | 20.0 | 18.0 | 19.0 | 9.0 | 21.0 | 20.0 | 26.0 | 31.0 | 8.0 | 19.0 | 9.0 | 20.0 | 42.0 | 16.0 | 19.0 | 10.0 | 24.0 | 16.0 | 8.0 | 11.0 | 12.0 | 13.0 | 14.0 | 19.0 | 19.0 | 20.0 | 21.0 | 10.0 | 20.0 | 20.0 |
| 4 | f0point5 | 0.4585654 | 0.12886968 | 0.5319149 | 0.37313432 | 0.42105263 | 0.4878049 | 0.35714287 | 0.30864197 | 0.47008547 | 0.3939394 | 0.46875 | 0.5504587 | 0.45454547 | 0.44642857 | 0.1891892 | 0.4945055 | 0.48192772 | 0.703125 | 0.3773585 | 0.5633803 | 0.6944444 | 0.6547619 | 0.625 | 0.6451613 | 0.23809524 | 0.31746033 | 0.47619048 | 0.4854369 | 0.37313432 | 0.47619048 | 0.42372882 | 0.2739726 |
| 5 | f1 | 0.45208272 | 0.10941253 | 0.5 | 0.35714287 | 0.45714286 | 0.47058824 | 0.36363637 | 0.33333334 | 0.45833334 | 0.45614034 | 0.42857143 | 0.55813956 | 0.47058824 | 0.5 | 0.25 | 0.5294118 | 0.45714286 | 0.64285713 | 0.4 | 0.5 | 0.5555556 | 0.6666667 | 0.5714286 | 0.55172414 | 0.22222222 | 0.2962963 | 0.51282054 | 0.5 | 0.32258064 | 0.44444445 | 0.5 | 0.2857143 |
| 6 | f2 | 0.45544168 | 0.109869495 | 0.4716981 | 0.34246576 | 0.5 | 0.45454547 | 0.37037036 | 0.36231884 | 0.44715446 | 0.5416667 | 0.39473686 | 0.5660377 | 0.4878049 | 0.5681818 | 0.36842105 | 0.56962025 | 0.4347826 | 0.59210527 | 0.42553192 | 0.4494382 | 0.46296296 | 0.67901236 | 0.5263158 | 0.48192772 | 0.20833333 | 0.2777778 | 0.5555556 | 0.5154639 | 0.2840909 | 0.41666666 | 0.6097561 | 0.29850745 |
| 7 | lift_top_group | 8.434459 | 6.230976 | 3.939394 | 5.7777777 | 5.7777777 | 9.62963 | 10.833333 | 0.0 | 0.0 | 4.126984 | 21.666666 | 8.253968 | 0.0 | 5.4166665 | 6.6666665 | 11.555555 | 4.5614033 | 10.833333 | 0.0 | 9.087719 | 21.583334 | 5.3958335 | 16.1875 | 9.592592 | 8.633333 | 5.7555556 | 5.0784316 | 13.631579 | 0.0 | 17.266666 | 18.5 | 13.282051 |
| 8 | logloss | 0.18275614 | 0.040141854 | 0.2298881 | 0.1870015 | 0.17362078 | 0.11766719 | 0.20026842 | 0.1715455 | 0.28538716 | 0.2323894 | 0.11934501 | 0.21354096 | 0.117949486 | 0.16674668 | 0.18554808 | 0.16184057 | 0.22426705 | 0.15844771 | 0.21751909 | 0.20606394 | 0.13357936 | 0.14009245 | 0.15569183 | 0.19382924 | 0.15572 | 0.2101646 | 0.1841546 | 0.20235649 | 0.24735378 | 0.14955007 | 0.15893105 | 0.18222404 |
| 9 | max_per_class_error | 0.53472584 | 0.12580356 | 0.54545456 | 0.6666667 | 0.46666667 | 0.5555556 | 0.625 | 0.61538464 | 0.56 | 0.3809524 | 0.625 | 0.42857143 | 0.5 | 0.375 | 0.46153846 | 0.4 | 0.57894737 | 0.4375 | 0.5555556 | 0.57894737 | 0.5833333 | 0.3125 | 0.5 | 0.5555556 | 0.8 | 0.73333335 | 0.4117647 | 0.47368422 | 0.7368421 | 0.6 | 0.2857143 | 0.6923077 |
| 10 | mcc | 0.42437366 | 0.115450345 | 0.46149588 | 0.32167214 | 0.42380953 | 0.4535989 | 0.3207254 | 0.29626045 | 0.40377942 | 0.41245824 | 0.41761234 | 0.51849735 | 0.4535989 | 0.47125202 | 0.23037261 | 0.500954 | 0.4200517 | 0.6301775 | 0.35240635 | 0.47790462 | 0.57656926 | 0.644346 | 0.5538034 | 0.54475677 | 0.19588113 | 0.25989273 | 0.4784671 | 0.45894626 | 0.2902304 | 0.42753962 | 0.48833007 | 0.24581943 |
| 11 | mean_per_class_accuracy | 0.7129822 | 0.059633225 | 0.710466 | 0.65034014 | 0.7421769 | 0.7142541 | 0.664959 | 0.6680162 | 0.6944681 | 0.76140666 | 0.68154764 | 0.7647938 | 0.74007934 | 0.78381145 | 0.6963563 | 0.77959186 | 0.6939288 | 0.77510244 | 0.69317657 | 0.70010966 | 0.706309 | 0.8314043 | 0.74176955 | 0.7159982 | 0.5879518 | 0.6169399 | 0.76932424 | 0.7402412 | 0.61699563 | 0.69196784 | 0.8244898 | 0.63148844 |
| 12 | mean_per_class_error | 0.28701785 | 0.059633225 | 0.289534 | 0.34965986 | 0.25782314 | 0.28574592 | 0.335041 | 0.3319838 | 0.30553192 | 0.23859334 | 0.3184524 | 0.23520622 | 0.25992063 | 0.21618852 | 0.30364373 | 0.22040816 | 0.3060712 | 0.22489753 | 0.30682343 | 0.29989034 | 0.29369095 | 0.16859567 | 0.25823045 | 0.28400186 | 0.4120482 | 0.3830601 | 0.23067574 | 0.25975877 | 0.3830044 | 0.30803213 | 0.1755102 | 0.36851156 |
| 13 | mse | 0.047355767 | 0.012567001 | 0.06382859 | 0.048395813 | 0.045665175 | 0.02705913 | 0.05125579 | 0.043924384 | 0.08046174 | 0.064537674 | 0.025397476 | 0.05710306 | 0.026595104 | 0.04519472 | 0.047526736 | 0.042032704 | 0.05879822 | 0.039143503 | 0.057704255 | 0.054364808 | 0.03243974 | 0.037947834 | 0.040817946 | 0.049748722 | 0.03732957 | 0.053175677 | 0.050824016 | 0.0531947 | 0.065268874 | 0.03416144 | 0.040912606 | 0.04586297 |
| 14 | null_deviance | 118.04616 | 23.000517 | 153.41394 | 114.7255 | 114.7255 | 81.936905 | 120.223526 | 103.75808 | 170.14055 | 147.85797 | 76.50513 | 147.85797 | 76.50513 | 120.223526 | 103.75808 | 114.7255 | 136.7752 | 120.223526 | 131.12575 | 136.65318 | 98.16257 | 120.09978 | 120.09978 | 131.12575 | 87.25086 | 114.60118 | 125.607956 | 136.65318 | 136.65318 | 87.25086 | 109.11213 | 103.63261 |
| 15 | pr_auc | 0.3222073 | 0.117995195 | 0.36176524 | 0.27658582 | 0.2827835 | 0.3236176 | 0.3070849 | 0.18546432 | 0.27349818 | 0.32504827 | 0.33809873 | 0.37322724 | 0.19861536 | 0.33749688 | 0.13608016 | 0.35717705 | 0.24309729 | 0.49718252 | 0.21438697 | 0.40590668 | 0.5233386 | 0.5116593 | 0.53263366 | 0.44188142 | 0.117747374 | 0.15523599 | 0.2929804 | 0.45021644 | 0.16748863 | 0.30866483 | 0.4587418 | 0.26851428 |
| 16 | precision | 0.46850044 | 0.15325205 | 0.5555556 | 0.3846154 | 0.4 | 0.5 | 0.3529412 | 0.29411766 | 0.47826087 | 0.3611111 | 0.5 | 0.54545456 | 0.44444445 | 0.41666666 | 0.1627907 | 0.47368422 | 0.5 | 0.75 | 0.36363637 | 0.61538464 | 0.8333333 | 0.64705884 | 0.6666667 | 0.72727275 | 0.25 | 0.33333334 | 0.45454547 | 0.47619048 | 0.41666666 | 0.5 | 0.3846154 | 0.26666668 |
| 17 | r2 | 0.154179 | 0.092566304 | 0.17593347 | 0.10978044 | 0.16000928 | 0.1902624 | 0.11247663 | 0.07527611 | 0.07417642 | 0.13075382 | 0.14837833 | 0.23088925 | 0.108219735 | 0.21742754 | -5.628634E-4 | 0.22682703 | 0.13195899 | 0.32220778 | 0.107685745 | 0.20025313 | 0.26582655 | 0.345273 | 0.29575396 | 0.23070674 | -0.0056645945 | 0.025388615 | 0.17128688 | 0.21746625 | 0.03984619 | 0.079685315 | 0.1998663 | 0.037981912 |
| 18 | recall | 0.46527413 | 0.12580356 | 0.45454547 | 0.33333334 | 0.53333336 | 0.44444445 | 0.375 | 0.3846154 | 0.44 | 0.61904764 | 0.375 | 0.5714286 | 0.5 | 0.625 | 0.53846157 | 0.6 | 0.42105263 | 0.5625 | 0.44444445 | 0.42105263 | 0.41666666 | 0.6875 | 0.5 | 0.44444445 | 0.2 | 0.26666668 | 0.5882353 | 0.5263158 | 0.2631579 | 0.4 | 0.71428573 | 0.30769232 |
| 19 | residual_deviance | 94.864044 | 20.856312 | 119.54181 | 97.240776 | 90.2828 | 61.18694 | 104.13957 | 89.20366 | 148.40134 | 120.84249 | 62.059406 | 111.0413 | 61.333733 | 86.70827 | 96.485 | 84.1571 | 116.618866 | 82.392815 | 112.67489 | 106.74112 | 69.19411 | 72.56789 | 80.64837 | 100.40354 | 80.66296 | 108.865265 | 95.39208 | 104.820656 | 128.12926 | 77.466934 | 82.32628 | 94.39205 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:15:43 | 0.000 sec | 2 | .88E1 | 15.0 | 0.452032 | 0.452336 | 0.452449 | 0.016049 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:15:43 | 0.003 sec | 4 | .54E1 | 15.0 | 0.450543 | 0.451081 | 0.451026 | 0.015982 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:15:43 | 0.006 sec | 6 | .34E1 | 15.0 | 0.448201 | 0.44911 | 0.448785 | 0.015878 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:15:43 | 0.009 sec | 8 | .21E1 | 15.0 | 0.444557 | 0.446049 | 0.445294 | 0.015719 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:15:43 | 0.012 sec | 10 | .13E1 | 15.0 | 0.439054 | 0.441445 | 0.440008 | 0.015485 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:15:43 | 0.015 sec | 12 | .81E0 | 15.0 | 0.431095 | 0.434831 | 0.432333 | 0.015162 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:15:43 | 0.018 sec | 14 | .5E0 | 15.0 | 0.420427 | 0.42608 | 0.421976 | 0.014764 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:15:43 | 0.021 sec | 16 | .31E0 | 15.0 | 0.407826 | 0.416001 | 0.409618 | 0.014367 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:15:43 | 0.024 sec | 18 | .19E0 | 15.0 | 0.395291 | 0.406462 | 0.397189 | 0.01409 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:15:43 | 0.026 sec | 20 | .12E0 | 15.0 | 0.384925 | 0.399334 | 0.386847 | 0.014012 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:15:43 | 0.032 sec | 22 | .75E-1 | 15.0 | 0.377522 | 0.395213 | 0.379491 | 0.0141 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:15:43 | 0.035 sec | 24 | .46E-1 | 15.0 | 0.372663 | 0.393571 | 0.37476 | 0.014266 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:15:43 | 0.038 sec | 26 | .29E-1 | 15.0 | 0.369573 | 0.393566 | 0.371865 | 0.014439 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:15:43 | 0.040 sec | 28 | .18E-1 | 15.0 | 0.367616 | 0.394478 | 0.370127 | 0.014582 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:15:43 | 0.294 sec | 29 | None | NaN | 29.0 | 0.21796 | 0.183191 | 0.159102 | 0.791675 | 0.31069 | 9.384835 | 0.076805 | 0.225261 | 0.1979 | 0.101607 | 0.704808 | 0.209112 | 4.160256 | 0.072933 | ||||||
| 15 | 2021-07-15 20:15:43 | 0.043 sec | 30 | .11E-1 | 15.0 | 0.366382 | 0.395799 | 0.369108 | 0.014691 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:15:43 | 0.045 sec | 32 | .69E-2 | 15.0 | 0.365629 | 0.397189 | 0.36977 | 0.015101 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:15:43 | 0.048 sec | 34 | .43E-2 | 15.0 | 0.365196 | 0.39844 | 0.369456 | 0.015149 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:15:43 | 0.051 sec | 36 | .27E-2 | 15.0 | 0.364964 | 0.399451 | 0.3722 | 0.015561 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.562218 | 1.000000 | 0.264181 |
| 1 | Average_Transaction_Frequency | 0.332237 | 0.590940 | 0.156115 |
| 2 | Merchant_ID | 0.211489 | 0.376169 | 0.099377 |
| 3 | Minimum_Transaction_Amount | 0.184079 | 0.327415 | 0.086497 |
| 4 | Channel_ID | 0.168276 | 0.299307 | 0.079071 |
| 5 | Card_Type.1 | 0.129030 | 0.229503 | 0.060630 |
| 6 | Card_Type.0 | 0.127603 | 0.226964 | 0.059960 |
| 7 | Transaction_Amount | 0.116948 | 0.208012 | 0.054953 |
| 8 | Maximum_Transaction_Amount | 0.070362 | 0.125151 | 0.033063 |
| 9 | Average_Transaction_Amount | 0.062486 | 0.111142 | 0.029362 |
| 10 | Transaction_Date | 0.055920 | 0.099462 | 0.026276 |
| 11 | Day | 0.055797 | 0.099244 | 0.026218 |
| 12 | Month | 0.042350 | 0.075327 | 0.019900 |
| 13 | City_ID | 0.009360 | 0.016648 | 0.004398 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201548 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.004148 ) | nlambda = 30, lambda.max = 8.4768, lambda.min = 0.004148, lambda.1... | 14 | 14 | 34 | automl_training_py_758_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.048289006094643254 RMSE: 0.2197475963341653 LogLoss: 0.18674920890842625 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938257 Residual deviance: 2908.0586811220137 AIC: 2938.0586811220137 AUC: 0.7714738042013254 AUCPR: 0.28467185733905365 Gini: 0.5429476084026508 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.26267937290624194:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7014.0 | 304.0 | 0.0415 | (304.0/7318.0) |
| 1 | 1 | 274.0 | 194.0 | 0.5855 | (274.0/468.0) |
| 2 | Total | 7288.0 | 498.0 | 0.0742 | (578.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.262679 | 0.401656 | 152.0 |
| 1 | max f2 | 0.068441 | 0.438630 | 226.0 |
| 2 | max f0point5 | 0.326639 | 0.405746 | 114.0 |
| 3 | max accuracy | 0.617895 | 0.940663 | 7.0 |
| 4 | max precision | 0.617895 | 0.875000 | 7.0 |
| 5 | max recall | 0.015002 | 1.000000 | 385.0 |
| 6 | max specificity | 0.821220 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.262679 | 0.362323 | 152.0 |
| 8 | max min_per_class_accuracy | 0.042229 | 0.688303 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.049012 | 0.712460 | 261.0 |
| 10 | max tns | 0.821220 | 7317.000000 | 0.0 |
| 11 | max fns | 0.821220 | 468.000000 | 0.0 |
| 12 | max fps | 0.000563 | 7318.000000 | 399.0 |
| 13 | max tps | 0.015002 | 468.000000 | 385.0 |
| 14 | max tnr | 0.821220 | 0.999863 | 0.0 |
| 15 | max fnr | 0.821220 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000563 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015002 | 1.000000 | 385.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.446342 | 7.465209 | 7.465209 | 0.448718 | 0.512333 | 0.448718 | 0.512333 | 0.074786 | 0.074786 | 646.520929 | 646.520929 | 0.068910 |
| 1 | 2 | 0.020036 | 0.399392 | 8.105084 | 7.785147 | 0.487179 | 0.421496 | 0.467949 | 0.466915 | 0.081197 | 0.155983 | 710.508437 | 678.514683 | 0.144641 |
| 2 | 3 | 0.030054 | 0.373092 | 6.825334 | 7.465209 | 0.410256 | 0.386868 | 0.448718 | 0.440232 | 0.068376 | 0.224359 | 582.533421 | 646.520929 | 0.206731 |
| 3 | 4 | 0.040072 | 0.350099 | 6.612043 | 7.251918 | 0.397436 | 0.361962 | 0.435897 | 0.420665 | 0.066239 | 0.290598 | 561.204252 | 625.191760 | 0.266548 |
| 4 | 5 | 0.050090 | 0.322964 | 5.972167 | 6.995968 | 0.358974 | 0.337287 | 0.420513 | 0.403989 | 0.059829 | 0.350427 | 497.216743 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.068398 | 2.908224 | 4.954720 | 0.174807 | 0.162481 | 0.297818 | 0.283390 | 0.145299 | 0.495726 | 190.822402 | 395.471951 | 0.420979 |
| 6 | 7 | 0.150013 | 0.053882 | 0.940896 | 3.617924 | 0.056555 | 0.059395 | 0.217466 | 0.208789 | 0.047009 | 0.542735 | -5.910399 | 261.792384 | 0.417838 |
| 7 | 8 | 0.200103 | 0.049021 | 1.023800 | 2.968560 | 0.061538 | 0.051152 | 0.178434 | 0.169329 | 0.051282 | 0.594017 | 2.380013 | 196.856039 | 0.419106 |
| 8 | 9 | 0.300026 | 0.043641 | 0.705672 | 2.214910 | 0.042416 | 0.046064 | 0.133134 | 0.128276 | 0.070513 | 0.664530 | -29.432799 | 121.491007 | 0.387815 |
| 9 | 10 | 0.400077 | 0.039629 | 0.640696 | 1.821230 | 0.038511 | 0.041541 | 0.109470 | 0.106585 | 0.064103 | 0.728632 | -35.930351 | 82.123033 | 0.349567 |
| 10 | 11 | 0.500000 | 0.036404 | 0.705672 | 1.598291 | 0.042416 | 0.038002 | 0.096070 | 0.092879 | 0.070513 | 0.799145 | -29.432799 | 59.829060 | 0.318276 |
| 11 | 12 | 0.600051 | 0.033297 | 0.533914 | 1.420819 | 0.032092 | 0.034843 | 0.085402 | 0.083202 | 0.053419 | 0.852564 | -46.608626 | 42.081852 | 0.268661 |
| 12 | 13 | 0.699974 | 0.030323 | 0.513216 | 1.291256 | 0.030848 | 0.031820 | 0.077615 | 0.075867 | 0.051282 | 0.903846 | -48.678400 | 29.125618 | 0.216910 |
| 13 | 14 | 0.800026 | 0.026729 | 0.555270 | 1.199214 | 0.033376 | 0.028636 | 0.072082 | 0.069961 | 0.055556 | 0.959402 | -44.472971 | 19.921363 | 0.169568 |
| 14 | 15 | 0.899949 | 0.022079 | 0.171072 | 1.085057 | 0.010283 | 0.024513 | 0.065220 | 0.064914 | 0.017094 | 0.976496 | -82.892800 | 8.505719 | 0.081442 |
| 15 | 16 | 1.000000 | 0.000405 | 0.234922 | 1.000000 | 0.014121 | 0.016874 | 0.060108 | 0.060108 | 0.023504 | 1.000000 | -76.507795 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.0472843107319236 RMSE: 0.21744955905203303 LogLoss: 0.18258494683106727 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311492 Residual deviance: 710.9857829601758 AIC: 740.9857829601758 AUC: 0.7771729484844239 AUCPR: 0.3121174131080186 Gini: 0.5543458969688477 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.11745592062307943:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1745.0 | 85.0 | 0.0464 | (85.0/1830.0) |
| 1 | 1 | 59.0 | 58.0 | 0.5043 | (59.0/117.0) |
| 2 | Total | 1804.0 | 143.0 | 0.074 | (144.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.117456 | 0.446154 | 120.0 |
| 1 | max f2 | 0.117456 | 0.474632 | 120.0 |
| 2 | max f0point5 | 0.351787 | 0.448578 | 72.0 |
| 3 | max accuracy | 0.459153 | 0.943503 | 20.0 |
| 4 | max precision | 0.852553 | 1.000000 | 0.0 |
| 5 | max recall | 0.015981 | 1.000000 | 374.0 |
| 6 | max specificity | 0.852553 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.117456 | 0.409306 | 120.0 |
| 8 | max min_per_class_accuracy | 0.042913 | 0.700855 | 232.0 |
| 9 | max mean_per_class_accuracy | 0.065856 | 0.730160 | 157.0 |
| 10 | max tns | 0.852553 | 1830.000000 | 0.0 |
| 11 | max fns | 0.852553 | 116.000000 | 0.0 |
| 12 | max fps | 0.000720 | 1830.000000 | 399.0 |
| 13 | max tps | 0.015981 | 117.000000 | 374.0 |
| 14 | max tnr | 0.852553 | 1.000000 | 0.0 |
| 15 | max fnr | 0.852553 | 0.991453 | 0.0 |
| 16 | max fpr | 0.000720 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015981 | 1.000000 | 374.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.13 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.461658 | 10.816667 | 10.816667 | 0.650000 | 0.559760 | 0.650000 | 0.559760 | 0.111111 | 0.111111 | 981.666667 | 981.666667 | 0.107286 |
| 1 | 2 | 0.020031 | 0.409938 | 5.255061 | 8.107166 | 0.315789 | 0.432911 | 0.487179 | 0.497962 | 0.051282 | 0.162393 | 425.506073 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.387704 | 7.488462 | 7.897436 | 0.450000 | 0.400431 | 0.474576 | 0.464901 | 0.076923 | 0.239316 | 648.846154 | 689.743590 | 0.222376 |
| 3 | 4 | 0.040062 | 0.357789 | 7.882591 | 7.893820 | 0.473684 | 0.372268 | 0.474359 | 0.442336 | 0.076923 | 0.316239 | 688.259109 | 689.381986 | 0.293835 |
| 4 | 5 | 0.050334 | 0.331068 | 4.992308 | 7.301675 | 0.300000 | 0.344861 | 0.438776 | 0.422443 | 0.051282 | 0.367521 | 399.230769 | 630.167452 | 0.337467 |
| 5 | 6 | 0.100154 | 0.066620 | 3.259582 | 5.290993 | 0.195876 | 0.164843 | 0.317949 | 0.294304 | 0.162393 | 0.529915 | 225.958234 | 429.099277 | 0.457237 |
| 6 | 7 | 0.149974 | 0.054771 | 0.857785 | 3.818318 | 0.051546 | 0.060851 | 0.229452 | 0.216753 | 0.042735 | 0.572650 | -14.221517 | 281.831753 | 0.449699 |
| 7 | 8 | 0.200308 | 0.049091 | 1.018838 | 3.114859 | 0.061224 | 0.051449 | 0.187179 | 0.175215 | 0.051282 | 0.623932 | 1.883830 | 211.485865 | 0.450708 |
| 8 | 9 | 0.299949 | 0.043363 | 0.600449 | 2.279593 | 0.036082 | 0.046001 | 0.136986 | 0.132291 | 0.059829 | 0.683761 | -39.955062 | 127.959255 | 0.408351 |
| 9 | 10 | 0.400103 | 0.039541 | 0.597370 | 1.858497 | 0.035897 | 0.041420 | 0.111682 | 0.109544 | 0.059829 | 0.743590 | -40.262985 | 85.849709 | 0.365448 |
| 10 | 11 | 0.500257 | 0.036243 | 0.768047 | 1.640183 | 0.046154 | 0.037892 | 0.098563 | 0.095199 | 0.076923 | 0.820513 | -23.195266 | 64.018323 | 0.340731 |
| 11 | 12 | 0.599897 | 0.033401 | 0.428892 | 1.438993 | 0.025773 | 0.034861 | 0.086473 | 0.085177 | 0.042735 | 0.863248 | -57.110759 | 43.899280 | 0.280188 |
| 12 | 13 | 0.700051 | 0.030201 | 0.256016 | 1.269748 | 0.015385 | 0.031833 | 0.076302 | 0.077546 | 0.025641 | 0.888889 | -74.398422 | 26.974810 | 0.200911 |
| 13 | 14 | 0.799692 | 0.026873 | 0.257335 | 1.143603 | 0.015464 | 0.028691 | 0.068722 | 0.071458 | 0.025641 | 0.914530 | -74.266455 | 14.360292 | 0.122180 |
| 14 | 15 | 0.899846 | 0.022130 | 0.597370 | 1.082806 | 0.035897 | 0.024683 | 0.065068 | 0.066252 | 0.059829 | 0.974359 | -40.262985 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.000532 | 0.256016 | 1.000000 | 0.015385 | 0.016614 | 0.060092 | 0.061281 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048552666458230254 RMSE: 0.22034669604564133 LogLoss: 0.188345720129101 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.7108723067327 Residual deviance: 2932.919553850361 AIC: 2962.919553850361 AUC: 0.761282039602619 AUCPR: 0.2749058838379849 Gini: 0.522564079205238 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.21739392712223063:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7017.0 | 301.0 | 0.0411 | (301.0/7318.0) |
| 1 | 1 | 275.0 | 193.0 | 0.5876 | (275.0/468.0) |
| 2 | Total | 7292.0 | 494.0 | 0.074 | (576.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.217394 | 0.401247 | 170.0 |
| 1 | max f2 | 0.069202 | 0.431267 | 229.0 |
| 2 | max f0point5 | 0.321750 | 0.404858 | 124.0 |
| 3 | max accuracy | 0.628953 | 0.940534 | 6.0 |
| 4 | max precision | 0.628953 | 0.857143 | 6.0 |
| 5 | max recall | 0.015297 | 1.000000 | 384.0 |
| 6 | max specificity | 0.836232 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.217394 | 0.362003 | 170.0 |
| 8 | max min_per_class_accuracy | 0.044181 | 0.680787 | 282.0 |
| 9 | max mean_per_class_accuracy | 0.069202 | 0.705086 | 229.0 |
| 10 | max tns | 0.836232 | 7317.000000 | 0.0 |
| 11 | max fns | 0.836232 | 468.000000 | 0.0 |
| 12 | max fps | 0.000536 | 7318.000000 | 399.0 |
| 13 | max tps | 0.015297 | 468.000000 | 384.0 |
| 14 | max tnr | 0.836232 | 0.999863 | 0.0 |
| 15 | max fnr | 0.836232 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000536 | 1.000000 | 399.0 |
| 17 | max tpr | 0.015297 | 1.000000 | 384.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.04 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.445582 | 7.891793 | 7.891793 | 0.474359 | 0.511634 | 0.474359 | 0.511634 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.399572 | 7.038626 | 7.465209 | 0.423077 | 0.420327 | 0.448718 | 0.465980 | 0.070513 | 0.149573 | 603.862590 | 646.520929 | 0.137821 |
| 2 | 3 | 0.030054 | 0.369078 | 7.038626 | 7.323015 | 0.423077 | 0.383300 | 0.440171 | 0.438420 | 0.070513 | 0.220085 | 603.862590 | 632.301483 | 0.202184 |
| 3 | 4 | 0.040072 | 0.344145 | 6.398751 | 7.091949 | 0.384615 | 0.356969 | 0.426282 | 0.418057 | 0.064103 | 0.284188 | 539.875082 | 609.194883 | 0.259728 |
| 4 | 5 | 0.050090 | 0.312867 | 6.398751 | 6.953309 | 0.384615 | 0.330172 | 0.417949 | 0.400480 | 0.064103 | 0.348291 | 539.875082 | 595.330923 | 0.317271 |
| 5 | 6 | 0.100051 | 0.065016 | 2.694384 | 4.826580 | 0.161954 | 0.141201 | 0.290116 | 0.271007 | 0.134615 | 0.482906 | 169.438402 | 382.658021 | 0.407339 |
| 6 | 7 | 0.179296 | 0.060058 | 0.916774 | 3.098535 | 0.055105 | 0.060564 | 0.186246 | 0.177996 | 0.072650 | 0.555556 | -8.322598 | 209.853550 | 0.400322 |
| 7 | 8 | 0.200103 | 0.055244 | 0.616176 | 2.840421 | 0.037037 | 0.057367 | 0.170732 | 0.165453 | 0.012821 | 0.568376 | -38.382399 | 184.042110 | 0.391825 |
| 8 | 9 | 0.300026 | 0.046015 | 0.876744 | 2.186422 | 0.052699 | 0.049757 | 0.131421 | 0.126921 | 0.087607 | 0.655983 | -12.325599 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.041356 | 0.640696 | 1.799867 | 0.038511 | 0.043558 | 0.108186 | 0.106073 | 0.064103 | 0.720085 | -35.930351 | 79.986692 | 0.340474 |
| 10 | 11 | 0.500000 | 0.037617 | 0.662904 | 1.572650 | 0.039846 | 0.039419 | 0.094529 | 0.092753 | 0.066239 | 0.786325 | -33.709599 | 57.264957 | 0.304636 |
| 11 | 12 | 0.600051 | 0.034282 | 0.619340 | 1.413697 | 0.037227 | 0.035901 | 0.084974 | 0.083273 | 0.061966 | 0.848291 | -38.066006 | 41.369662 | 0.264115 |
| 12 | 13 | 0.699974 | 0.030997 | 0.513216 | 1.285151 | 0.030848 | 0.032601 | 0.077248 | 0.076040 | 0.051282 | 0.899573 | -48.678400 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.027363 | 0.512557 | 1.188530 | 0.030809 | 0.029265 | 0.071440 | 0.070190 | 0.051282 | 0.950855 | -48.744281 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.022573 | 0.277992 | 1.087431 | 0.016710 | 0.025096 | 0.065363 | 0.065183 | 0.027778 | 0.978632 | -72.200800 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000389 | 0.213565 | 1.000000 | 0.012837 | 0.017364 | 0.060108 | 0.060399 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9350129 | 0.01858018 | 0.9076923 | 0.9461538 | 0.9269231 | 0.93846154 | 0.91923076 | 0.96153843 | 0.90384614 | 0.9230769 | 0.9653846 | 0.91923076 | 0.90384614 | 0.9692308 | 0.95384616 | 0.9423077 | 0.9307692 | 0.93846154 | 0.95752895 | 0.93050194 | 0.93436295 | 0.95366794 | 0.9189189 | 0.94208497 | 0.96138996 | 0.9459459 | 0.9227799 | 0.9111969 | 0.94208497 | 0.93822396 | 0.9266409 | 0.9150579 |
| 1 | auc | 0.76908827 | 0.068760455 | 0.73873186 | 0.81319165 | 0.76108843 | 0.6821209 | 0.7308362 | 0.765121 | 0.7605907 | 0.6327083 | 0.89823127 | 0.7470346 | 0.8762807 | 0.81653225 | 0.80138737 | 0.88682 | 0.7628558 | 0.8825071 | 0.7665317 | 0.7707827 | 0.7520138 | 0.7259475 | 0.7531012 | 0.7126329 | 0.75335276 | 0.7245902 | 0.82653064 | 0.6099125 | 0.79879516 | 0.8561011 | 0.7443522 | 0.721965 |
| 2 | err | 0.06498713 | 0.01858018 | 0.092307694 | 0.053846154 | 0.073076926 | 0.06153846 | 0.08076923 | 0.03846154 | 0.09615385 | 0.07692308 | 0.034615386 | 0.08076923 | 0.09615385 | 0.03076923 | 0.046153847 | 0.057692308 | 0.06923077 | 0.06153846 | 0.042471044 | 0.06949807 | 0.06563707 | 0.046332046 | 0.08108108 | 0.057915058 | 0.038610037 | 0.054054055 | 0.077220075 | 0.08880309 | 0.057915058 | 0.06177606 | 0.07335907 | 0.08494209 |
| 3 | err_count | 16.866667 | 4.826174 | 24.0 | 14.0 | 19.0 | 16.0 | 21.0 | 10.0 | 25.0 | 20.0 | 9.0 | 21.0 | 25.0 | 8.0 | 12.0 | 15.0 | 18.0 | 16.0 | 11.0 | 18.0 | 17.0 | 12.0 | 21.0 | 15.0 | 10.0 | 14.0 | 20.0 | 23.0 | 15.0 | 16.0 | 19.0 | 22.0 |
| 4 | f0point5 | 0.46883494 | 0.11115612 | 0.44117647 | 0.32786885 | 0.37974682 | 0.375 | 0.3773585 | 0.5555556 | 0.37190083 | 0.46052632 | 0.71428573 | 0.42857143 | 0.39285713 | 0.6818182 | 0.42553192 | 0.42168674 | 0.4878049 | 0.57894737 | 0.5208333 | 0.48387095 | 0.6111111 | 0.5 | 0.48387095 | 0.45454547 | 0.65789473 | 0.52238804 | 0.5154639 | 0.2027027 | 0.3846154 | 0.53763443 | 0.42857143 | 0.3409091 |
| 5 | f1 | 0.45175734 | 0.09444865 | 0.42857143 | 0.36363637 | 0.38709676 | 0.27272728 | 0.43243244 | 0.44444445 | 0.41860464 | 0.4117647 | 0.6666667 | 0.46153846 | 0.4680851 | 0.6 | 0.4 | 0.4827586 | 0.47058824 | 0.57894737 | 0.47619048 | 0.5 | 0.5641026 | 0.33333334 | 0.46153846 | 0.4827586 | 0.5 | 0.5 | 0.5 | 0.20689656 | 0.44444445 | 0.5555556 | 0.38709676 | 0.3529412 |
| 6 | f2 | 0.44871268 | 0.10561504 | 0.41666666 | 0.40816328 | 0.39473686 | 0.21428572 | 0.5063291 | 0.37037036 | 0.4787234 | 0.3723404 | 0.625 | 0.5 | 0.57894737 | 0.53571427 | 0.3773585 | 0.5645161 | 0.45454547 | 0.57894737 | 0.4385965 | 0.51724136 | 0.52380955 | 0.25 | 0.44117647 | 0.5147059 | 0.4032258 | 0.47945204 | 0.4854369 | 0.2112676 | 0.5263158 | 0.57471263 | 0.3529412 | 0.36585367 |
| 7 | lift_top_group | 8.254969 | 5.394905 | 3.939394 | 0.0 | 5.7777777 | 5.4166665 | 6.1904764 | 14.444445 | 5.098039 | 4.3333335 | 17.333334 | 5.098039 | 10.833333 | 14.444445 | 15.757576 | 15.757576 | 4.814815 | 4.5614033 | 21.583334 | 5.0784316 | 7.848485 | 12.333333 | 8.222222 | 6.6410255 | 12.333333 | 11.511111 | 4.111111 | 0.0 | 8.633333 | 10.156863 | 0.0 | 5.3958335 |
| 8 | logloss | 0.18698037 | 0.034593187 | 0.25308558 | 0.13322039 | 0.18778352 | 0.22053827 | 0.1778893 | 0.15815605 | 0.20848903 | 0.24610427 | 0.14476691 | 0.19869351 | 0.18314655 | 0.14154497 | 0.14809966 | 0.12631863 | 0.21055041 | 0.1816795 | 0.16084102 | 0.19125155 | 0.23212363 | 0.18728253 | 0.23025249 | 0.16716476 | 0.16715868 | 0.18065415 | 0.22116658 | 0.20417723 | 0.13301413 | 0.18278328 | 0.22538288 | 0.20609161 |
| 9 | max_per_class_error | 0.5465412 | 0.12645437 | 0.59090906 | 0.5555556 | 0.6 | 0.8125 | 0.42857143 | 0.6666667 | 0.47058824 | 0.65 | 0.4 | 0.47058824 | 0.3125 | 0.5 | 0.6363636 | 0.36363637 | 0.5555556 | 0.42105263 | 0.5833333 | 0.47058824 | 0.5 | 0.78571427 | 0.5714286 | 0.46153846 | 0.64285713 | 0.53333336 | 0.52380955 | 0.78571427 | 0.4 | 0.4117647 | 0.6666667 | 0.625 |
| 10 | mcc | 0.42926165 | 0.09689645 | 0.37899473 | 0.34270737 | 0.34849003 | 0.28041983 | 0.4057413 | 0.45453402 | 0.37859425 | 0.37876385 | 0.6531435 | 0.4227232 | 0.44904926 | 0.59769464 | 0.37829927 | 0.46958143 | 0.43454942 | 0.54575235 | 0.45964268 | 0.4636061 | 0.534389 | 0.3854678 | 0.41942987 | 0.455169 | 0.5307148 | 0.47293204 | 0.45894626 | 0.1600278 | 0.4324185 | 0.5234039 | 0.3543969 | 0.30826613 |
| 11 | mean_per_class_accuracy | 0.70962584 | 0.05912186 | 0.6814362 | 0.7042939 | 0.67959183 | 0.58760244 | 0.7552265 | 0.66263443 | 0.72972643 | 0.66041666 | 0.79387754 | 0.7379569 | 0.8027664 | 0.74596775 | 0.671778 | 0.79609346 | 0.7056933 | 0.7728762 | 0.70023614 | 0.7440447 | 0.73734176 | 0.60510206 | 0.6953781 | 0.75093806 | 0.6765306 | 0.7210382 | 0.7191877 | 0.58265305 | 0.77791166 | 0.7755226 | 0.65214384 | 0.66280866 |
| 12 | mean_per_class_error | 0.29037416 | 0.05912186 | 0.3185638 | 0.29570606 | 0.32040817 | 0.41239753 | 0.24477352 | 0.3373656 | 0.27027354 | 0.33958334 | 0.20612244 | 0.2620431 | 0.1972336 | 0.25403225 | 0.32822198 | 0.20390654 | 0.2943067 | 0.22712383 | 0.29976383 | 0.25595528 | 0.26265824 | 0.39489797 | 0.30462185 | 0.24906191 | 0.3234694 | 0.27896175 | 0.28081232 | 0.41734692 | 0.22208835 | 0.2244774 | 0.34785616 | 0.33719134 |
| 13 | mse | 0.0483371 | 0.010323445 | 0.0687803 | 0.033440076 | 0.0481677 | 0.056031913 | 0.045046337 | 0.03854647 | 0.055476002 | 0.06474829 | 0.036839753 | 0.052673783 | 0.049887624 | 0.034134537 | 0.03654746 | 0.031405274 | 0.055607446 | 0.04932925 | 0.038625963 | 0.050440677 | 0.062008012 | 0.046495285 | 0.06148921 | 0.042071782 | 0.0413082 | 0.045086056 | 0.06096714 | 0.050274875 | 0.032439683 | 0.04923463 | 0.059544906 | 0.053464297 |
| 14 | null_deviance | 118.0237 | 19.516905 | 153.41394 | 81.936905 | 114.7255 | 120.223526 | 109.23703 | 98.28862 | 125.73113 | 142.31174 | 114.7255 | 125.73113 | 120.223526 | 98.28862 | 92.82863 | 92.82863 | 131.24835 | 136.7752 | 98.16257 | 125.607956 | 153.29364 | 109.11213 | 147.73709 | 103.63261 | 109.11213 | 114.60118 | 147.73709 | 109.11213 | 87.25086 | 125.607956 | 131.12575 | 120.09978 |
| 15 | pr_auc | 0.31203422 | 0.11883964 | 0.25553325 | 0.14307661 | 0.24645944 | 0.15377098 | 0.22888662 | 0.32257372 | 0.2574186 | 0.2181343 | 0.6503489 | 0.28860432 | 0.3593786 | 0.44209298 | 0.3435926 | 0.42935067 | 0.29370108 | 0.47450367 | 0.39289847 | 0.29267287 | 0.46296886 | 0.3009195 | 0.37841904 | 0.21956538 | 0.3479928 | 0.35085404 | 0.3560301 | 0.088813365 | 0.2109929 | 0.45519468 | 0.2003933 | 0.19588514 |
| 16 | precision | 0.49351957 | 0.14984153 | 0.45 | 0.30769232 | 0.375 | 0.5 | 0.3478261 | 0.6666667 | 0.34615386 | 0.5 | 0.75 | 0.4090909 | 0.3548387 | 0.75 | 0.44444445 | 0.3888889 | 0.5 | 0.57894737 | 0.5555556 | 0.47368422 | 0.64705884 | 0.75 | 0.5 | 0.4375 | 0.8333333 | 0.53846157 | 0.5263158 | 0.2 | 0.3529412 | 0.5263158 | 0.46153846 | 0.33333334 |
| 17 | r2 | 0.13871059 | 0.07129163 | 0.11200372 | -6.857817E-4 | 0.1139764 | 0.029775254 | 0.11581524 | 0.12441486 | 0.09218643 | 0.088128306 | 0.32234904 | 0.1380422 | 0.13616718 | 0.22463213 | 0.097988956 | 0.22490084 | 0.13703781 | 0.27174985 | 0.12582044 | 0.17753744 | 0.20223257 | 0.090685084 | 0.17471835 | 0.11750556 | 0.1921296 | 0.17365639 | 0.18172532 | 0.016767057 | 0.1260697 | 0.19720268 | 0.079222746 | 0.07756214 |
| 18 | recall | 0.45345882 | 0.12645437 | 0.4090909 | 0.44444445 | 0.4 | 0.1875 | 0.5714286 | 0.33333334 | 0.5294118 | 0.35 | 0.6 | 0.5294118 | 0.6875 | 0.5 | 0.36363637 | 0.6363636 | 0.44444445 | 0.57894737 | 0.41666666 | 0.5294118 | 0.5 | 0.21428572 | 0.42857143 | 0.53846157 | 0.35714287 | 0.46666667 | 0.47619048 | 0.21428572 | 0.6 | 0.5882353 | 0.33333334 | 0.375 |
| 19 | residual_deviance | 97.05051 | 17.939524 | 131.6045 | 69.274605 | 97.64744 | 114.6799 | 92.50244 | 82.24115 | 108.4143 | 127.97422 | 75.27879 | 103.32063 | 95.236206 | 73.603386 | 77.011826 | 65.685684 | 109.48621 | 94.47334 | 83.31565 | 99.068306 | 120.240036 | 97.01235 | 119.27079 | 86.59134 | 86.588196 | 93.57885 | 114.564285 | 105.7638 | 68.90131 | 94.68174 | 116.74833 | 106.755455 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:15:57 | 0.000 sec | 2 | .85E1 | 15 | 0.452180 | 0.451850 | 0.452528 | 0.013665 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:15:57 | 0.006 sec | 4 | .53E1 | 15 | 0.450780 | 0.450304 | 0.451192 | 0.013616 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:15:57 | 0.009 sec | 6 | .33E1 | 15 | 0.448581 | 0.447873 | 0.449090 | 0.013540 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:15:57 | 0.015 sec | 8 | .2E1 | 15 | 0.445162 | 0.444091 | 0.445818 | 0.013424 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:15:57 | 0.018 sec | 10 | .13E1 | 15 | 0.440008 | 0.438383 | 0.440874 | 0.013253 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:15:57 | 0.022 sec | 12 | .78E0 | 15 | 0.432579 | 0.430142 | 0.433719 | 0.013016 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:15:57 | 0.026 sec | 14 | .49E0 | 15 | 0.422671 | 0.419120 | 0.424114 | 0.012721 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:15:57 | 0.034 sec | 16 | .3E0 | 15 | 0.411053 | 0.406141 | 0.412745 | 0.012415 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:15:57 | 0.038 sec | 18 | .19E0 | 15 | 0.399613 | 0.393295 | 0.401427 | 0.012179 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:15:57 | 0.044 sec | 20 | .12E0 | 15 | 0.390281 | 0.382757 | 0.392139 | 0.012071 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:15:57 | 0.048 sec | 22 | .72E-1 | 15 | 0.383727 | 0.375349 | 0.385643 | 0.012083 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:15:57 | 0.052 sec | 24 | .45E-1 | 15 | 0.379532 | 0.370670 | 0.381560 | 0.012171 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:15:57 | 0.056 sec | 26 | .28E-1 | 15 | 0.376950 | 0.367921 | 0.379136 | 0.012293 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:15:57 | 0.060 sec | 28 | .17E-1 | 15 | 0.375368 | 0.366399 | 0.377726 | 0.012425 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:15:57 | 0.065 sec | 30 | .11E-1 | 15 | 0.374405 | 0.365624 | 0.377024 | 0.012563 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:15:57 | 0.070 sec | 32 | .67E-2 | 15 | 0.373829 | 0.365281 | 0.376979 | 0.012763 | 0.0 | 32.0 | 0.219748 | 0.186749 | 0.145251 | 0.771474 | 0.284672 | 7.465209 | 0.074236 | 0.21745 | 0.182585 | 0.162833 | 0.777173 | 0.312117 | 10.816667 | 0.07396 | |
| 16 | 2021-07-15 20:15:57 | 0.076 sec | 34 | .41E-2 | 15 | 0.373498 | 0.365170 | 0.376708 | 0.012847 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:15:57 | 0.082 sec | 36 | .26E-2 | 15 | 0.373314 | 0.365167 | 0.379378 | 0.013551 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:15:57 | 0.085 sec | 37 | .16E-2 | 15 | 0.373211 | 0.365209 | 0.379266 | 0.013585 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:15:57 | 0.089 sec | 38 | .99E-3 | 15 | 0.373145 | 0.365264 | 0.382975 | 0.014216 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.574569 | 1.000000 | 0.251354 |
| 1 | Average_Transaction_Frequency | 0.330039 | 0.574411 | 0.144381 |
| 2 | Merchant_ID | 0.228274 | 0.397296 | 0.099862 |
| 3 | Card_Type.1 | 0.188364 | 0.327836 | 0.082403 |
| 4 | Card_Type.0 | 0.183994 | 0.320229 | 0.080491 |
| 5 | Minimum_Transaction_Amount | 0.178538 | 0.310734 | 0.078104 |
| 6 | Channel_ID | 0.156764 | 0.272837 | 0.068579 |
| 7 | Transaction_Date | 0.118429 | 0.206119 | 0.051809 |
| 8 | Maximum_Transaction_Amount | 0.107535 | 0.187158 | 0.047043 |
| 9 | Transaction_Amount | 0.102343 | 0.178121 | 0.044771 |
| 10 | Day | 0.042072 | 0.073223 | 0.018405 |
| 11 | Average_Transaction_Amount | 0.037674 | 0.065569 | 0.016481 |
| 12 | Month | 0.030028 | 0.052261 | 0.013136 |
| 13 | City_ID | 0.007270 | 0.012653 | 0.003180 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201601 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.006648 ) | nlambda = 30, lambda.max = 8.4359, lambda.min = 0.006648, lambda.1... | 14 | 14 | 32 | automl_training_py_787_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04860004704867561 RMSE: 0.22045418355902346 LogLoss: 0.18821587587232166 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938443 Residual deviance: 2930.8976190837925 AIC: 2960.8976190837925 AUC: 0.7658389161019662 AUCPR: 0.27676303540696284 Gini: 0.5316778322039324 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13311874926458897:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6968.0 | 350.0 | 0.0478 | (350.0/7318.0) |
| 1 | 1 | 264.0 | 204.0 | 0.5641 | (264.0/468.0) |
| 2 | Total | 7232.0 | 554.0 | 0.0789 | (614.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.133119 | 0.399217 | 183.0 |
| 1 | max f2 | 0.062886 | 0.433247 | 229.0 |
| 2 | max f0point5 | 0.341660 | 0.405093 | 102.0 |
| 3 | max accuracy | 0.551175 | 0.940534 | 13.0 |
| 4 | max precision | 0.551175 | 0.666667 | 13.0 |
| 5 | max recall | 0.018828 | 1.000000 | 379.0 |
| 6 | max specificity | 0.816577 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.133119 | 0.358794 | 183.0 |
| 8 | max min_per_class_accuracy | 0.042328 | 0.690171 | 282.0 |
| 9 | max mean_per_class_accuracy | 0.062201 | 0.709210 | 230.0 |
| 10 | max tns | 0.816577 | 7317.000000 | 0.0 |
| 11 | max fns | 0.816577 | 468.000000 | 0.0 |
| 12 | max fps | 0.001414 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018828 | 468.000000 | 379.0 |
| 14 | max tnr | 0.816577 | 0.999863 | 0.0 |
| 15 | max fnr | 0.816577 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001414 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018828 | 1.000000 | 379.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.426170 | 8.531668 | 8.531668 | 0.512821 | 0.500239 | 0.512821 | 0.500239 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.386802 | 7.038626 | 7.785147 | 0.423077 | 0.403737 | 0.467949 | 0.451988 | 0.070513 | 0.155983 | 603.862590 | 678.514683 | 0.144641 |
| 2 | 3 | 0.030054 | 0.362283 | 6.612043 | 7.394112 | 0.397436 | 0.374698 | 0.444444 | 0.426225 | 0.066239 | 0.222222 | 561.204252 | 639.411206 | 0.204458 |
| 3 | 4 | 0.040072 | 0.341644 | 7.038626 | 7.305241 | 0.423077 | 0.352126 | 0.439103 | 0.407700 | 0.070513 | 0.292735 | 603.862590 | 630.524052 | 0.268821 |
| 4 | 5 | 0.050090 | 0.319431 | 5.545584 | 6.953309 | 0.333333 | 0.331388 | 0.417949 | 0.392438 | 0.055556 | 0.348291 | 454.558405 | 595.330923 | 0.317271 |
| 5 | 6 | 0.100051 | 0.065296 | 2.779920 | 4.869293 | 0.167095 | 0.158163 | 0.292683 | 0.275451 | 0.138889 | 0.487179 | 177.992002 | 386.929331 | 0.411886 |
| 6 | 7 | 0.150013 | 0.052631 | 0.855360 | 3.532461 | 0.051414 | 0.057324 | 0.212329 | 0.202804 | 0.042735 | 0.529915 | -14.463999 | 253.246107 | 0.404197 |
| 7 | 8 | 0.200103 | 0.047896 | 0.981142 | 2.893812 | 0.058974 | 0.049986 | 0.173941 | 0.164551 | 0.049145 | 0.579060 | -1.885821 | 189.381247 | 0.403192 |
| 8 | 9 | 0.300026 | 0.043339 | 0.791208 | 2.193544 | 0.047558 | 0.045400 | 0.131849 | 0.124868 | 0.079060 | 0.658120 | -20.879199 | 119.354437 | 0.380995 |
| 9 | 10 | 0.400077 | 0.039890 | 0.683410 | 1.815889 | 0.041078 | 0.041551 | 0.109149 | 0.104032 | 0.068376 | 0.726496 | -31.659041 | 81.588948 | 0.347294 |
| 10 | 11 | 0.500000 | 0.037189 | 0.684288 | 1.589744 | 0.041131 | 0.038548 | 0.095556 | 0.090945 | 0.068376 | 0.794872 | -31.571199 | 58.974359 | 0.313729 |
| 11 | 12 | 0.600051 | 0.034528 | 0.619340 | 1.427940 | 0.037227 | 0.035893 | 0.085830 | 0.081766 | 0.061966 | 0.856838 | -38.066006 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.032056 | 0.342144 | 1.272940 | 0.020566 | 0.033266 | 0.076514 | 0.074842 | 0.034188 | 0.891026 | -65.785600 | 27.294048 | 0.203269 |
| 13 | 14 | 0.800026 | 0.028798 | 0.597983 | 1.188530 | 0.035944 | 0.030477 | 0.071440 | 0.069294 | 0.059829 | 0.950855 | -40.201661 | 18.853021 | 0.160475 |
| 14 | 15 | 0.899949 | 0.024486 | 0.320760 | 1.092180 | 0.019280 | 0.026746 | 0.065649 | 0.064570 | 0.032051 | 0.982906 | -67.924000 | 9.218010 | 0.088263 |
| 15 | 16 | 1.000000 | 0.001159 | 0.170852 | 1.000000 | 0.010270 | 0.019973 | 0.060108 | 0.060108 | 0.017094 | 1.000000 | -82.914760 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.046312757463764995 RMSE: 0.21520399035279295 LogLoss: 0.17830407476832813 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311494 Residual deviance: 694.31606714787 AIC: 724.31606714787 AUC: 0.8064663023679417 AUCPR: 0.3519823679479973 Gini: 0.6129326047358834 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.12546435225898123:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1744.0 | 86.0 | 0.047 | (86.0/1830.0) |
| 1 | 1 | 58.0 | 59.0 | 0.4957 | (58.0/117.0) |
| 2 | Total | 1802.0 | 145.0 | 0.074 | (144.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.125464 | 0.450382 | 120.0 |
| 1 | max f2 | 0.067541 | 0.489297 | 150.0 |
| 2 | max f0point5 | 0.340357 | 0.453515 | 67.0 |
| 3 | max accuracy | 0.415105 | 0.942989 | 12.0 |
| 4 | max precision | 0.826170 | 1.000000 | 0.0 |
| 5 | max recall | 0.017601 | 1.000000 | 377.0 |
| 6 | max specificity | 0.826170 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.125464 | 0.413941 | 120.0 |
| 8 | max min_per_class_accuracy | 0.044012 | 0.726496 | 221.0 |
| 9 | max mean_per_class_accuracy | 0.049205 | 0.751836 | 197.0 |
| 10 | max tns | 0.826170 | 1830.000000 | 0.0 |
| 11 | max fns | 0.826170 | 116.000000 | 0.0 |
| 12 | max fps | 0.001271 | 1830.000000 | 399.0 |
| 13 | max tps | 0.017601 | 117.000000 | 377.0 |
| 14 | max tnr | 0.826170 | 1.000000 | 0.0 |
| 15 | max fnr | 0.826170 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001271 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017601 | 1.000000 | 377.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.02 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.404767 | 9.984615 | 9.984615 | 0.600000 | 0.451144 | 0.600000 | 0.451144 | 0.102564 | 0.102564 | 898.461538 | 898.461538 | 0.098193 |
| 1 | 2 | 0.020031 | 0.373716 | 7.006748 | 8.533859 | 0.421053 | 0.389930 | 0.512821 | 0.421321 | 0.068376 | 0.170940 | 600.674764 | 753.385930 | 0.160558 |
| 2 | 3 | 0.030303 | 0.356649 | 8.320513 | 8.461538 | 0.500000 | 0.365352 | 0.508475 | 0.402349 | 0.085470 | 0.256410 | 732.051282 | 746.153846 | 0.240563 |
| 3 | 4 | 0.040062 | 0.342569 | 7.006748 | 8.107166 | 0.421053 | 0.349165 | 0.487179 | 0.389394 | 0.068376 | 0.324786 | 600.674764 | 710.716634 | 0.302928 |
| 4 | 5 | 0.050334 | 0.319697 | 5.824359 | 7.641287 | 0.350000 | 0.332737 | 0.459184 | 0.377831 | 0.059829 | 0.384615 | 482.435897 | 664.128728 | 0.355654 |
| 5 | 6 | 0.100154 | 0.064909 | 3.259582 | 5.461670 | 0.195876 | 0.175587 | 0.328205 | 0.277228 | 0.162393 | 0.547009 | 225.958234 | 446.166995 | 0.475424 |
| 6 | 7 | 0.149974 | 0.050940 | 1.200899 | 4.046277 | 0.072165 | 0.056862 | 0.243151 | 0.204024 | 0.059829 | 0.606838 | 20.089876 | 304.627678 | 0.486073 |
| 7 | 8 | 0.200308 | 0.047434 | 1.018838 | 3.285536 | 0.061224 | 0.049202 | 0.197436 | 0.165120 | 0.051282 | 0.658120 | 1.883830 | 228.553583 | 0.487081 |
| 8 | 9 | 0.299949 | 0.043070 | 0.772006 | 2.450562 | 0.046392 | 0.044963 | 0.147260 | 0.125205 | 0.076923 | 0.735043 | -22.799366 | 145.056200 | 0.462912 |
| 9 | 10 | 0.400103 | 0.039921 | 0.512032 | 1.965307 | 0.030769 | 0.041490 | 0.118100 | 0.104249 | 0.051282 | 0.786325 | -48.796844 | 96.530726 | 0.410915 |
| 10 | 11 | 0.500257 | 0.037128 | 0.597370 | 1.691439 | 0.035897 | 0.038513 | 0.101643 | 0.091088 | 0.059829 | 0.846154 | -40.262985 | 69.143895 | 0.368012 |
| 11 | 12 | 0.599897 | 0.034457 | 0.428892 | 1.481735 | 0.025773 | 0.035840 | 0.089041 | 0.081912 | 0.042735 | 0.888889 | -57.110759 | 48.173516 | 0.307468 |
| 12 | 13 | 0.700051 | 0.031804 | 0.256016 | 1.306375 | 0.015385 | 0.033123 | 0.078503 | 0.074932 | 0.025641 | 0.914530 | -74.398422 | 30.637545 | 0.228191 |
| 13 | 14 | 0.799692 | 0.028751 | 0.343114 | 1.186354 | 0.020619 | 0.030339 | 0.071291 | 0.069376 | 0.034188 | 0.948718 | -65.688607 | 18.635443 | 0.158554 |
| 14 | 15 | 0.899846 | 0.024609 | 0.170677 | 1.073308 | 0.010256 | 0.026944 | 0.064498 | 0.064653 | 0.017094 | 0.965812 | -82.932281 | 7.330816 | 0.070184 |
| 15 | 16 | 1.000000 | 0.001271 | 0.341354 | 1.000000 | 0.020513 | 0.019714 | 0.060092 | 0.060152 | 0.034188 | 1.000000 | -65.864563 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04890977665800232 RMSE: 0.2211555485580281 LogLoss: 0.18968704287312807 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3541.5280884950757 Residual deviance: 2953.806631620351 AIC: 2983.806631620351 AUC: 0.7525934179391408 AUCPR: 0.26292381686896604 Gini: 0.5051868358782816 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.21744089241429695:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6993.0 | 325.0 | 0.0444 | (325.0/7318.0) |
| 1 | 1 | 272.0 | 196.0 | 0.5812 | (272.0/468.0) |
| 2 | Total | 7265.0 | 521.0 | 0.0767 | (597.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.217441 | 0.396360 | 166.0 |
| 1 | max f2 | 0.061106 | 0.424945 | 234.0 |
| 2 | max f0point5 | 0.312078 | 0.396341 | 125.0 |
| 3 | max accuracy | 0.536786 | 0.940277 | 14.0 |
| 4 | max precision | 0.536786 | 0.578947 | 14.0 |
| 5 | max recall | 0.018427 | 1.000000 | 383.0 |
| 6 | max specificity | 0.827746 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.217441 | 0.356131 | 166.0 |
| 8 | max min_per_class_accuracy | 0.041953 | 0.677350 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.059585 | 0.706042 | 237.0 |
| 10 | max tns | 0.827746 | 7317.000000 | 0.0 |
| 11 | max fns | 0.827746 | 468.000000 | 0.0 |
| 12 | max fps | 0.001271 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018427 | 468.000000 | 383.0 |
| 14 | max tnr | 0.827746 | 0.999863 | 0.0 |
| 15 | max fnr | 0.827746 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001271 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018427 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.426666 | 7.891793 | 7.891793 | 0.474359 | 0.500716 | 0.474359 | 0.500716 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.387598 | 7.038626 | 7.465209 | 0.423077 | 0.403451 | 0.448718 | 0.452084 | 0.070513 | 0.149573 | 603.862590 | 646.520929 | 0.137821 |
| 2 | 3 | 0.030054 | 0.362533 | 6.612043 | 7.180820 | 0.397436 | 0.374686 | 0.431624 | 0.426284 | 0.066239 | 0.215812 | 561.204252 | 618.082037 | 0.197638 |
| 3 | 4 | 0.040072 | 0.341125 | 7.038626 | 7.145272 | 0.423077 | 0.351141 | 0.429487 | 0.407498 | 0.070513 | 0.286325 | 603.862590 | 614.527175 | 0.262001 |
| 4 | 5 | 0.050090 | 0.321071 | 5.332292 | 6.782676 | 0.320513 | 0.331653 | 0.407692 | 0.392329 | 0.053419 | 0.339744 | 433.229235 | 578.267587 | 0.308178 |
| 5 | 6 | 0.100051 | 0.064755 | 2.737152 | 4.762511 | 0.164524 | 0.158215 | 0.286264 | 0.275422 | 0.136752 | 0.476496 | 173.715202 | 376.251056 | 0.400519 |
| 6 | 7 | 0.150013 | 0.052463 | 1.069200 | 3.532461 | 0.064267 | 0.057330 | 0.212329 | 0.202787 | 0.053419 | 0.529915 | 6.920001 | 253.246107 | 0.404197 |
| 7 | 8 | 0.200103 | 0.048076 | 0.682533 | 2.819065 | 0.041026 | 0.050001 | 0.169448 | 0.164542 | 0.034188 | 0.564103 | -31.746658 | 181.906455 | 0.387278 |
| 8 | 9 | 0.300026 | 0.043358 | 0.748440 | 2.129447 | 0.044987 | 0.045488 | 0.127997 | 0.124891 | 0.074786 | 0.638889 | -25.155999 | 112.944730 | 0.360534 |
| 9 | 10 | 0.400077 | 0.039957 | 0.768836 | 1.789185 | 0.046213 | 0.041526 | 0.107544 | 0.104043 | 0.076923 | 0.715812 | -23.116421 | 78.918522 | 0.335927 |
| 10 | 11 | 0.500000 | 0.037262 | 0.555984 | 1.542735 | 0.033419 | 0.038565 | 0.092731 | 0.090957 | 0.055556 | 0.771368 | -44.401600 | 54.273504 | 0.288722 |
| 11 | 12 | 0.600051 | 0.034611 | 0.747479 | 1.410136 | 0.044929 | 0.035944 | 0.084760 | 0.081785 | 0.074786 | 0.846154 | -25.252076 | 41.013567 | 0.261841 |
| 12 | 13 | 0.699974 | 0.031948 | 0.320760 | 1.254625 | 0.019280 | 0.033342 | 0.075413 | 0.074869 | 0.032051 | 0.878205 | -67.924000 | 25.462479 | 0.189629 |
| 13 | 14 | 0.800026 | 0.028861 | 0.640696 | 1.177847 | 0.038511 | 0.030517 | 0.070798 | 0.069323 | 0.064103 | 0.942308 | -35.930351 | 17.784680 | 0.151381 |
| 14 | 15 | 0.899949 | 0.024504 | 0.363528 | 1.087431 | 0.021851 | 0.026817 | 0.065363 | 0.064603 | 0.036325 | 0.978632 | -63.647200 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000818 | 0.213565 | 1.000000 | 0.012837 | 0.020021 | 0.060108 | 0.060143 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9298931 | 0.0250966 | 0.9153846 | 0.8923077 | 0.9423077 | 0.9307692 | 0.9076923 | 0.9230769 | 0.96153843 | 0.88076925 | 0.9423077 | 0.9307692 | 0.8730769 | 0.9153846 | 0.95384616 | 0.91923076 | 0.95 | 0.8923077 | 0.9459459 | 0.9459459 | 0.9227799 | 0.93050194 | 0.9498069 | 0.93050194 | 0.980695 | 0.93050194 | 0.9459459 | 0.96138996 | 0.90733594 | 0.9189189 | 0.96138996 | 0.93436295 |
| 1 | auc | 0.7638155 | 0.07840959 | 0.65411437 | 0.65770835 | 0.627483 | 0.8719512 | 0.69851434 | 0.7614784 | 0.71337366 | 0.8090544 | 0.7856882 | 0.715 | 0.78107345 | 0.75416666 | 0.69146824 | 0.7910619 | 0.73829204 | 0.8360417 | 0.7558651 | 0.84333956 | 0.771261 | 0.7508631 | 0.7068306 | 0.7680959 | 0.95238096 | 0.79883385 | 0.7612245 | 0.931207 | 0.64634776 | 0.6961595 | 0.8420888 | 0.80349857 |
| 2 | err | 0.07010692 | 0.0250966 | 0.08461539 | 0.10769231 | 0.057692308 | 0.06923077 | 0.092307694 | 0.07692308 | 0.03846154 | 0.11923077 | 0.057692308 | 0.06923077 | 0.12692308 | 0.08461539 | 0.046153847 | 0.08076923 | 0.05 | 0.10769231 | 0.054054055 | 0.054054055 | 0.077220075 | 0.06949807 | 0.05019305 | 0.06949807 | 0.019305019 | 0.06949807 | 0.054054055 | 0.038610037 | 0.09266409 | 0.08108108 | 0.038610037 | 0.06563707 |
| 3 | err_count | 18.2 | 6.530565 | 22.0 | 28.0 | 15.0 | 18.0 | 24.0 | 20.0 | 10.0 | 31.0 | 15.0 | 18.0 | 33.0 | 22.0 | 12.0 | 21.0 | 13.0 | 28.0 | 14.0 | 14.0 | 20.0 | 18.0 | 13.0 | 18.0 | 5.0 | 18.0 | 14.0 | 10.0 | 24.0 | 21.0 | 10.0 | 17.0 |
| 4 | f0point5 | 0.44858864 | 0.11190734 | 0.41095892 | 0.3 | 0.3846154 | 0.42553192 | 0.3125 | 0.44444445 | 0.5555556 | 0.23809524 | 0.42168674 | 0.5147059 | 0.3716216 | 0.48387095 | 0.25 | 0.5188679 | 0.6451613 | 0.39285713 | 0.4225352 | 0.49382716 | 0.31578946 | 0.5813953 | 0.5319149 | 0.5 | 0.64102566 | 0.40697673 | 0.5 | 0.61538464 | 0.33653846 | 0.37037036 | 0.6122449 | 0.45918366 |
| 5 | f1 | 0.4460082 | 0.09835014 | 0.3529412 | 0.3 | 0.2857143 | 0.47058824 | 0.33333334 | 0.44444445 | 0.44444445 | 0.31111112 | 0.4827586 | 0.4375 | 0.4 | 0.5217391 | 0.25 | 0.5116279 | 0.55172414 | 0.44 | 0.46153846 | 0.53333336 | 0.375 | 0.5263158 | 0.4347826 | 0.5 | 0.6666667 | 0.4375 | 0.5 | 0.61538464 | 0.36842105 | 0.36363637 | 0.54545456 | 0.51428574 |
| 6 | f2 | 0.4561799 | 0.1084078 | 0.30927834 | 0.3 | 0.22727273 | 0.5263158 | 0.35714287 | 0.44444445 | 0.37037036 | 0.44871795 | 0.5645161 | 0.38043478 | 0.43307087 | 0.5660377 | 0.25 | 0.5045872 | 0.48192772 | 0.5 | 0.5084746 | 0.5797101 | 0.46153846 | 0.48076922 | 0.36764705 | 0.5 | 0.6944444 | 0.47297296 | 0.5 | 0.61538464 | 0.40697673 | 0.35714287 | 0.4918033 | 0.58441556 |
| 7 | lift_top_group | 7.708711 | 4.9579034 | 4.126984 | 4.3333335 | 11.555555 | 6.1904764 | 5.4166665 | 4.814815 | 14.444445 | 7.878788 | 7.878788 | 8.666667 | 7.2222223 | 8.666667 | 10.833333 | 3.939394 | 4.814815 | 8.666667 | 7.848485 | 6.6410255 | 0.0 | 11.772727 | 11.511111 | 0.0 | 24.666666 | 0.0 | 6.1666665 | 13.282051 | 5.3958335 | 5.0784316 | 13.282051 | 6.1666665 |
| 8 | logloss | 0.18837565 | 0.046021946 | 0.26287267 | 0.25981933 | 0.20707157 | 0.15901293 | 0.20903116 | 0.21858045 | 0.16082983 | 0.15486749 | 0.13977513 | 0.23682101 | 0.28132218 | 0.22464626 | 0.13725281 | 0.229805 | 0.20063104 | 0.21669544 | 0.14379609 | 0.14639567 | 0.15626031 | 0.23566772 | 0.18534091 | 0.20660925 | 0.085883446 | 0.17014194 | 0.16715579 | 0.12906426 | 0.20655727 | 0.21879189 | 0.1444281 | 0.15614255 |
| 9 | max_per_class_error | 0.5281965 | 0.13092676 | 0.71428573 | 0.7 | 0.8 | 0.42857143 | 0.625 | 0.5555556 | 0.6666667 | 0.36363637 | 0.36363637 | 0.65 | 0.5416667 | 0.4 | 0.75 | 0.5 | 0.5555556 | 0.45 | 0.45454547 | 0.3846154 | 0.45454547 | 0.54545456 | 0.6666667 | 0.5 | 0.2857143 | 0.5 | 0.5 | 0.3846154 | 0.5625 | 0.64705884 | 0.53846157 | 0.35714287 |
| 10 | mcc | 0.41977817 | 0.10289098 | 0.32058987 | 0.24166666 | 0.29156 | 0.442711 | 0.28644824 | 0.40312213 | 0.45453402 | 0.3151945 | 0.46958143 | 0.4180421 | 0.33369943 | 0.48112524 | 0.22619048 | 0.46777904 | 0.5448534 | 0.39270213 | 0.4395816 | 0.5103111 | 0.358294 | 0.497063 | 0.4334265 | 0.4626556 | 0.6583247 | 0.4046794 | 0.47142857 | 0.5950594 | 0.3244902 | 0.32054776 | 0.5357057 | 0.49197817 |
| 11 | mean_per_class_accuracy | 0.71577567 | 0.06263985 | 0.6282128 | 0.62083334 | 0.59387755 | 0.76132405 | 0.65881145 | 0.7015611 | 0.66263443 | 0.76396495 | 0.79609346 | 0.6645833 | 0.6867938 | 0.7708333 | 0.6130952 | 0.72899157 | 0.71602386 | 0.73541665 | 0.7545821 | 0.7893996 | 0.74248534 | 0.7146145 | 0.6605191 | 0.7313278 | 0.85119045 | 0.72755104 | 0.73571426 | 0.7975297 | 0.6878858 | 0.6558094 | 0.72467166 | 0.7969388 |
| 12 | mean_per_class_error | 0.2842243 | 0.06263985 | 0.37178722 | 0.37916666 | 0.40612245 | 0.23867595 | 0.34118852 | 0.29843894 | 0.3373656 | 0.23603505 | 0.20390654 | 0.33541667 | 0.31320623 | 0.22916667 | 0.38690478 | 0.2710084 | 0.28397614 | 0.26458332 | 0.2454179 | 0.21060038 | 0.25751466 | 0.2853855 | 0.33948088 | 0.2686722 | 0.14880952 | 0.272449 | 0.2642857 | 0.20247029 | 0.3121142 | 0.34419057 | 0.27532834 | 0.20306122 |
| 13 | mse | 0.04862412 | 0.013597094 | 0.06979529 | 0.06830732 | 0.05171563 | 0.041861404 | 0.053489566 | 0.057113327 | 0.038551603 | 0.03989301 | 0.03369533 | 0.061574847 | 0.077920444 | 0.059966493 | 0.03097597 | 0.062460918 | 0.052104812 | 0.0593873 | 0.03485129 | 0.03767627 | 0.03919019 | 0.062438797 | 0.046099585 | 0.055794932 | 0.018379116 | 0.04398521 | 0.04234867 | 0.032988574 | 0.053164594 | 0.056817733 | 0.035669677 | 0.040505677 |
| 14 | null_deviance | 118.050934 | 23.736769 | 147.85797 | 142.31174 | 114.7255 | 109.23703 | 120.223526 | 131.24835 | 98.28862 | 92.82863 | 92.82863 | 142.31174 | 164.55519 | 142.31174 | 76.50513 | 153.41394 | 131.24835 | 142.31174 | 92.701996 | 103.63261 | 92.701996 | 153.29364 | 114.60118 | 131.12575 | 70.953766 | 109.11213 | 109.11213 | 103.63261 | 120.09978 | 125.607956 | 103.63261 | 109.11213 |
| 15 | pr_auc | 0.29471502 | 0.10256198 | 0.17643054 | 0.17327827 | 0.23371911 | 0.3080065 | 0.19179678 | 0.2770317 | 0.31584054 | 0.18853465 | 0.30463576 | 0.33001462 | 0.2897747 | 0.37070388 | 0.095832385 | 0.39227492 | 0.30173638 | 0.36744025 | 0.28214458 | 0.36823717 | 0.15240371 | 0.41413847 | 0.35748693 | 0.30841172 | 0.5376247 | 0.23415504 | 0.30992687 | 0.4182569 | 0.15552099 | 0.17333405 | 0.4647606 | 0.3479982 |
| 16 | precision | 0.45862922 | 0.13680108 | 0.46153846 | 0.3 | 0.5 | 0.4 | 0.3 | 0.44444445 | 0.6666667 | 0.20588236 | 0.3888889 | 0.5833333 | 0.3548387 | 0.46153846 | 0.25 | 0.52380955 | 0.72727275 | 0.36666667 | 0.4 | 0.47058824 | 0.2857143 | 0.625 | 0.625 | 0.5 | 0.625 | 0.3888889 | 0.5 | 0.61538464 | 0.3181818 | 0.375 | 0.6666667 | 0.42857143 |
| 17 | r2 | 0.13678008 | 0.08098339 | 0.0599399 | 0.038005315 | 0.048713826 | 0.17833017 | 0.0737975 | 0.11366827 | 0.12429824 | 0.015419005 | 0.16838104 | 0.1328209 | 0.07001727 | 0.15547188 | -0.03867833 | 0.19359091 | 0.19139455 | 0.16362886 | 0.1430134 | 0.20970546 | 0.036320638 | 0.19669026 | 0.15508026 | 0.13721073 | 0.30108303 | 0.139775 | 0.17178096 | 0.30803418 | 0.082733005 | 0.07355605 | 0.25179562 | 0.20782469 |
| 18 | recall | 0.47180352 | 0.13092676 | 0.2857143 | 0.3 | 0.2 | 0.5714286 | 0.375 | 0.44444445 | 0.33333334 | 0.6363636 | 0.6363636 | 0.35 | 0.45833334 | 0.6 | 0.25 | 0.5 | 0.44444445 | 0.55 | 0.54545456 | 0.61538464 | 0.54545456 | 0.45454547 | 0.33333334 | 0.5 | 0.71428573 | 0.5 | 0.5 | 0.61538464 | 0.4375 | 0.3529412 | 0.46153846 | 0.64285713 |
| 19 | residual_deviance | 97.79852 | 23.972954 | 136.69379 | 135.10606 | 107.677216 | 82.68672 | 108.696205 | 113.661835 | 83.63151 | 80.5311 | 72.68306 | 123.14693 | 146.28754 | 116.81605 | 71.37146 | 119.4986 | 104.32814 | 112.68163 | 74.486374 | 75.83296 | 80.94284 | 122.07588 | 96.00659 | 107.02359 | 44.487625 | 88.13352 | 86.5867 | 66.855286 | 106.996666 | 113.3342 | 74.81376 | 80.88184 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:16:08 | 0.000 sec | 2 | .84E1 | 15 | 0.452197 | 0.451770 | 0.452576 | 0.016585 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:16:08 | 0.003 sec | 4 | .52E1 | 15 | 0.450809 | 0.450177 | 0.451248 | 0.016553 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:16:08 | 0.006 sec | 6 | .33E1 | 15 | 0.448628 | 0.447671 | 0.449160 | 0.016505 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:16:08 | 0.009 sec | 8 | .2E1 | 15 | 0.445238 | 0.443773 | 0.445911 | 0.016432 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:16:08 | 0.013 sec | 10 | .13E1 | 15 | 0.440132 | 0.437888 | 0.441006 | 0.016329 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:16:08 | 0.016 sec | 12 | .78E0 | 15 | 0.432783 | 0.429384 | 0.433917 | 0.016195 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:16:08 | 0.019 sec | 14 | .48E0 | 15 | 0.423013 | 0.417994 | 0.424430 | 0.016049 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:16:08 | 0.022 sec | 16 | .3E0 | 15 | 0.411619 | 0.404526 | 0.413260 | 0.015941 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:16:08 | 0.025 sec | 18 | .19E0 | 15 | 0.400496 | 0.391048 | 0.402241 | 0.015930 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:16:08 | 0.028 sec | 20 | .12E0 | 15 | 0.391528 | 0.379714 | 0.393318 | 0.016034 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:16:08 | 0.031 sec | 22 | .72E-1 | 15 | 0.385324 | 0.371359 | 0.387195 | 0.016209 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:16:08 | 0.035 sec | 24 | .45E-1 | 15 | 0.381428 | 0.365653 | 0.383452 | 0.016394 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:16:08 | 0.038 sec | 26 | .28E-1 | 15 | 0.379094 | 0.361876 | 0.381325 | 0.016552 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:16:08 | 0.041 sec | 28 | .17E-1 | 15 | 0.377712 | 0.359378 | 0.380171 | 0.016668 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:16:08 | 0.044 sec | 30 | .11E-1 | 15 | 0.376901 | 0.357716 | 0.379578 | 0.016746 | 0.0 | 30.0 | 0.220454 | 0.188216 | 0.139745 | 0.765839 | 0.276763 | 8.531668 | 0.078859 | 0.215204 | 0.178304 | 0.180034 | 0.806466 | 0.351982 | 9.984615 | 0.07396 | |
| 15 | 2021-07-15 20:16:08 | 0.048 sec | 32 | .66E-2 | 15 | 0.376432 | 0.356608 | 0.379300 | 0.016798 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:16:08 | 0.051 sec | 34 | .41E-2 | 15 | 0.376167 | 0.355871 | 0.381958 | 0.017568 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:16:08 | 0.053 sec | 35 | .26E-2 | 15 | 0.376022 | 0.355392 | 0.386745 | 0.017616 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:16:08 | 0.055 sec | 36 | .16E-2 | 15 | 0.375937 | 0.355078 | 0.388969 | 0.017383 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.557628 | 1.000000 | 0.275816 |
| 1 | Average_Transaction_Frequency | 0.248575 | 0.445773 | 0.122951 |
| 2 | Merchant_ID | 0.222683 | 0.399339 | 0.110144 |
| 3 | Minimum_Transaction_Amount | 0.169187 | 0.303404 | 0.083684 |
| 4 | Channel_ID | 0.152286 | 0.273096 | 0.075324 |
| 5 | Card_Type.1 | 0.147552 | 0.264606 | 0.072982 |
| 6 | Card_Type.0 | 0.145044 | 0.260110 | 0.071742 |
| 7 | Transaction_Amount | 0.100957 | 0.181048 | 0.049936 |
| 8 | Transaction_Date | 0.087762 | 0.157385 | 0.043409 |
| 9 | Maximum_Transaction_Amount | 0.077205 | 0.138453 | 0.038187 |
| 10 | Average_Transaction_Amount | 0.040019 | 0.071767 | 0.019794 |
| 11 | Month | 0.033688 | 0.060412 | 0.016663 |
| 12 | Day | 0.030488 | 0.054675 | 0.015080 |
| 13 | City_ID | 0.008665 | 0.015539 | 0.004286 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201611 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.01083 ) | nlambda = 30, lambda.max = 8.5337, lambda.min = 0.01083, lambda.1s... | 14 | 14 | 30 | automl_training_py_816_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04823195162840978 RMSE: 0.21961773978531374 LogLoss: 0.18737757487126636 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938493 Residual deviance: 2917.84359589536 AIC: 2947.84359589536 AUC: 0.768613803220253 AUCPR: 0.2914684517502117 Gini: 0.5372276064405059 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.17708488095539351:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6987.0 | 331.0 | 0.0452 | (331.0/7318.0) |
| 1 | 1 | 265.0 | 203.0 | 0.5662 | (265.0/468.0) |
| 2 | Total | 7252.0 | 534.0 | 0.0765 | (596.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.177085 | 0.405190 | 170.0 |
| 1 | max f2 | 0.070990 | 0.431335 | 220.0 |
| 2 | max f0point5 | 0.307233 | 0.406137 | 127.0 |
| 3 | max accuracy | 0.436718 | 0.941562 | 35.0 |
| 4 | max precision | 0.838103 | 1.000000 | 0.0 |
| 5 | max recall | 0.020512 | 1.000000 | 382.0 |
| 6 | max specificity | 0.838103 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.177085 | 0.365379 | 170.0 |
| 8 | max min_per_class_accuracy | 0.042185 | 0.690171 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.061932 | 0.705005 | 232.0 |
| 10 | max tns | 0.838103 | 7318.000000 | 0.0 |
| 11 | max fns | 0.838103 | 467.000000 | 0.0 |
| 12 | max fps | 0.002470 | 7318.000000 | 399.0 |
| 13 | max tps | 0.020512 | 468.000000 | 382.0 |
| 14 | max tnr | 0.838103 | 1.000000 | 0.0 |
| 15 | max fnr | 0.838103 | 0.997863 | 0.0 |
| 16 | max fpr | 0.002470 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020512 | 1.000000 | 382.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.415798 | 9.171543 | 9.171543 | 0.551282 | 0.483535 | 0.551282 | 0.483535 | 0.091880 | 0.091880 | 817.154284 | 817.154284 | 0.087098 |
| 1 | 2 | 0.020036 | 0.381606 | 7.251918 | 8.211730 | 0.435897 | 0.397340 | 0.493590 | 0.440437 | 0.072650 | 0.164530 | 625.191760 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.360463 | 6.398751 | 7.607404 | 0.384615 | 0.370441 | 0.457265 | 0.417105 | 0.064103 | 0.228632 | 539.875082 | 660.740375 | 0.211278 |
| 3 | 4 | 0.040072 | 0.341410 | 6.185459 | 7.251918 | 0.371795 | 0.350264 | 0.435897 | 0.400395 | 0.061966 | 0.290598 | 518.545913 | 625.191760 | 0.266548 |
| 4 | 5 | 0.050090 | 0.321318 | 5.545584 | 6.910651 | 0.333333 | 0.331879 | 0.415385 | 0.386692 | 0.055556 | 0.346154 | 454.558405 | 591.065089 | 0.314998 |
| 5 | 6 | 0.100051 | 0.065115 | 2.737152 | 4.826580 | 0.164524 | 0.159658 | 0.290116 | 0.273320 | 0.136752 | 0.482906 | 173.715202 | 382.658021 | 0.407339 |
| 6 | 7 | 0.150013 | 0.051512 | 0.598752 | 3.418511 | 0.035990 | 0.056567 | 0.205479 | 0.201131 | 0.029915 | 0.512821 | -40.124800 | 241.851071 | 0.386010 |
| 7 | 8 | 0.200103 | 0.047032 | 1.023800 | 2.819065 | 0.061538 | 0.048967 | 0.169448 | 0.163041 | 0.051282 | 0.564103 | 2.380013 | 181.906455 | 0.387278 |
| 8 | 9 | 0.300026 | 0.042605 | 1.090584 | 2.243398 | 0.065553 | 0.044484 | 0.134846 | 0.123556 | 0.108974 | 0.673077 | 9.058401 | 124.339766 | 0.396909 |
| 9 | 10 | 0.400077 | 0.039574 | 0.555270 | 1.821230 | 0.033376 | 0.041026 | 0.109470 | 0.102917 | 0.055556 | 0.728632 | -44.472971 | 82.123033 | 0.349567 |
| 10 | 11 | 0.500000 | 0.037019 | 0.684288 | 1.594017 | 0.041131 | 0.038283 | 0.095813 | 0.090000 | 0.068376 | 0.797009 | -31.571199 | 59.401709 | 0.316003 |
| 11 | 12 | 0.600051 | 0.034776 | 0.640696 | 1.435062 | 0.038511 | 0.035896 | 0.086259 | 0.080979 | 0.064103 | 0.861111 | -35.930351 | 43.506231 | 0.277755 |
| 12 | 13 | 0.699974 | 0.032511 | 0.491832 | 1.300414 | 0.029563 | 0.033637 | 0.078165 | 0.074221 | 0.049145 | 0.910256 | -50.816800 | 30.041402 | 0.223730 |
| 13 | 14 | 0.800026 | 0.029921 | 0.427131 | 1.191201 | 0.025674 | 0.031295 | 0.071601 | 0.068852 | 0.042735 | 0.952991 | -57.286901 | 19.120107 | 0.162748 |
| 14 | 15 | 0.899949 | 0.026373 | 0.213840 | 1.082683 | 0.012853 | 0.028320 | 0.065078 | 0.064352 | 0.021368 | 0.974359 | -78.616000 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.002069 | 0.256279 | 1.000000 | 0.015404 | 0.021933 | 0.060108 | 0.060108 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.047985632167582545 RMSE: 0.21905623060662427 LogLoss: 0.18268542350885467 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311503 Residual deviance: 711.3770391434801 AIC: 741.3770391434801 AUC: 0.8016300032693475 AUCPR: 0.29132074353642756 Gini: 0.603260006538695 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.3167079295521701:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1763.0 | 67.0 | 0.0366 | (67.0/1830.0) |
| 1 | 1 | 66.0 | 51.0 | 0.5641 | (66.0/117.0) |
| 2 | Total | 1829.0 | 118.0 | 0.0683 | (133.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.316708 | 0.434043 | 96.0 |
| 1 | max f2 | 0.060726 | 0.512266 | 177.0 |
| 2 | max f0point5 | 0.349104 | 0.445434 | 69.0 |
| 3 | max accuracy | 0.550871 | 0.939908 | 5.0 |
| 4 | max precision | 0.550871 | 0.500000 | 5.0 |
| 5 | max recall | 0.025923 | 1.000000 | 361.0 |
| 6 | max specificity | 0.841333 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.316708 | 0.397699 | 96.0 |
| 8 | max min_per_class_accuracy | 0.043897 | 0.717949 | 242.0 |
| 9 | max mean_per_class_accuracy | 0.054595 | 0.766688 | 194.0 |
| 10 | max tns | 0.841333 | 1829.000000 | 0.0 |
| 11 | max fns | 0.841333 | 117.000000 | 0.0 |
| 12 | max fps | 0.002139 | 1830.000000 | 399.0 |
| 13 | max tps | 0.025923 | 117.000000 | 361.0 |
| 14 | max tnr | 0.841333 | 0.999454 | 0.0 |
| 15 | max fnr | 0.841333 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002139 | 1.000000 | 399.0 |
| 17 | max tpr | 0.025923 | 1.000000 | 361.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.41 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.436186 | 5.824359 | 5.824359 | 0.350000 | 0.531948 | 0.350000 | 0.531948 | 0.059829 | 0.059829 | 482.435897 | 482.435897 | 0.052725 |
| 1 | 2 | 0.020031 | 0.390530 | 7.882591 | 6.827087 | 0.473684 | 0.410857 | 0.410256 | 0.472955 | 0.076923 | 0.136752 | 688.259109 | 582.708744 | 0.124184 |
| 2 | 3 | 0.030303 | 0.368643 | 8.320513 | 7.333333 | 0.500000 | 0.379336 | 0.440678 | 0.441220 | 0.085470 | 0.222222 | 732.051282 | 633.333333 | 0.204189 |
| 3 | 4 | 0.040062 | 0.351706 | 9.634278 | 7.893820 | 0.578947 | 0.359572 | 0.474359 | 0.421331 | 0.094017 | 0.316239 | 863.427800 | 689.381986 | 0.293835 |
| 4 | 5 | 0.050334 | 0.335332 | 5.824359 | 7.471481 | 0.350000 | 0.343222 | 0.448980 | 0.405390 | 0.059829 | 0.376068 | 482.435897 | 647.148090 | 0.346560 |
| 5 | 6 | 0.100154 | 0.069834 | 3.259582 | 5.376331 | 0.195876 | 0.207422 | 0.323077 | 0.306914 | 0.162393 | 0.538462 | 225.958234 | 437.633136 | 0.466330 |
| 6 | 7 | 0.149974 | 0.052815 | 2.058684 | 4.274236 | 0.123711 | 0.058615 | 0.256849 | 0.224431 | 0.102564 | 0.641026 | 105.868358 | 327.423604 | 0.522446 |
| 7 | 8 | 0.200308 | 0.048196 | 0.339613 | 3.285536 | 0.020408 | 0.050131 | 0.197436 | 0.180632 | 0.017094 | 0.658120 | -66.038723 | 228.553583 | 0.487081 |
| 8 | 9 | 0.299949 | 0.043627 | 0.600449 | 2.393572 | 0.036082 | 0.045470 | 0.143836 | 0.135733 | 0.059829 | 0.717949 | -39.955062 | 139.357218 | 0.444725 |
| 9 | 10 | 0.400103 | 0.040057 | 0.512032 | 1.922583 | 0.030769 | 0.041912 | 0.115533 | 0.112247 | 0.051282 | 0.769231 | -48.796844 | 92.258319 | 0.392728 |
| 10 | 11 | 0.500257 | 0.037449 | 0.597370 | 1.657268 | 0.035897 | 0.038673 | 0.099589 | 0.097517 | 0.059829 | 0.829060 | -40.262985 | 65.726847 | 0.349825 |
| 11 | 12 | 0.599897 | 0.035113 | 0.428892 | 1.453240 | 0.025773 | 0.036297 | 0.087329 | 0.087349 | 0.042735 | 0.871795 | -57.110759 | 45.324025 | 0.289281 |
| 12 | 13 | 0.700051 | 0.032769 | 0.597370 | 1.330794 | 0.035897 | 0.033897 | 0.079971 | 0.079702 | 0.059829 | 0.931624 | -40.262985 | 33.079369 | 0.246378 |
| 13 | 14 | 0.799692 | 0.030156 | 0.257335 | 1.197042 | 0.015464 | 0.031564 | 0.071933 | 0.073704 | 0.025641 | 0.957265 | -74.266455 | 19.704231 | 0.167647 |
| 14 | 15 | 0.899846 | 0.026819 | 0.256016 | 1.092305 | 0.015385 | 0.028692 | 0.065639 | 0.068694 | 0.025641 | 0.982906 | -74.398422 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.002139 | 0.170677 | 1.000000 | 0.010256 | 0.022537 | 0.060092 | 0.064071 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04855670806539202 RMSE: 0.22035586687309242 LogLoss: 0.18885515660287583 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.550632499956 Residual deviance: 2940.8524986199823 AIC: 2970.8524986199823 AUC: 0.7553296169379798 AUCPR: 0.2753257589168883 Gini: 0.5106592338759597 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.17302916399128174:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6986.0 | 332.0 | 0.0454 | (332.0/7318.0) |
| 1 | 1 | 266.0 | 202.0 | 0.5684 | (266.0/468.0) |
| 2 | Total | 7252.0 | 534.0 | 0.0768 | (598.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.173029 | 0.403194 | 171.0 |
| 1 | max f2 | 0.078950 | 0.427215 | 213.0 |
| 2 | max f0point5 | 0.301819 | 0.398936 | 127.0 |
| 3 | max accuracy | 0.434048 | 0.940791 | 35.0 |
| 4 | max precision | 0.637468 | 0.833333 | 5.0 |
| 5 | max recall | 0.020084 | 1.000000 | 383.0 |
| 6 | max specificity | 0.851979 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.173029 | 0.363241 | 171.0 |
| 8 | max min_per_class_accuracy | 0.041443 | 0.679147 | 291.0 |
| 9 | max mean_per_class_accuracy | 0.065120 | 0.702054 | 228.0 |
| 10 | max tns | 0.851979 | 7317.000000 | 0.0 |
| 11 | max fns | 0.851979 | 468.000000 | 0.0 |
| 12 | max fps | 0.002275 | 7318.000000 | 399.0 |
| 13 | max tps | 0.020084 | 468.000000 | 383.0 |
| 14 | max tnr | 0.851979 | 0.999863 | 0.0 |
| 15 | max fnr | 0.851979 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002275 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020084 | 1.000000 | 383.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.02 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.415211 | 8.744959 | 8.744959 | 0.525641 | 0.484859 | 0.525641 | 0.484859 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.381113 | 7.038626 | 7.891793 | 0.423077 | 0.397062 | 0.474359 | 0.440961 | 0.070513 | 0.158120 | 603.862590 | 689.179268 | 0.146914 |
| 2 | 3 | 0.030054 | 0.360095 | 6.185459 | 7.323015 | 0.371795 | 0.369808 | 0.440171 | 0.417243 | 0.061966 | 0.220085 | 518.545913 | 632.301483 | 0.202184 |
| 3 | 4 | 0.040072 | 0.340605 | 6.398751 | 7.091949 | 0.384615 | 0.350495 | 0.426282 | 0.400556 | 0.064103 | 0.284188 | 539.875082 | 609.194883 | 0.259728 |
| 4 | 5 | 0.050090 | 0.322147 | 5.119001 | 6.697359 | 0.307692 | 0.331531 | 0.402564 | 0.386751 | 0.051282 | 0.335470 | 411.900066 | 569.735919 | 0.303631 |
| 5 | 6 | 0.100051 | 0.065220 | 2.865456 | 4.783867 | 0.172237 | 0.159533 | 0.287548 | 0.273288 | 0.143162 | 0.478632 | 186.545602 | 378.386711 | 0.402792 |
| 6 | 7 | 0.150013 | 0.051605 | 0.513216 | 3.361536 | 0.030848 | 0.056702 | 0.202055 | 0.201154 | 0.025641 | 0.504274 | -48.678400 | 236.153553 | 0.376916 |
| 7 | 8 | 0.200103 | 0.047094 | 1.023800 | 2.776351 | 0.061538 | 0.049043 | 0.166881 | 0.163078 | 0.051282 | 0.555556 | 2.380013 | 177.635145 | 0.378185 |
| 8 | 9 | 0.300026 | 0.042627 | 0.940896 | 2.165057 | 0.056555 | 0.044532 | 0.130137 | 0.123596 | 0.094017 | 0.649573 | -5.910399 | 116.505678 | 0.371901 |
| 9 | 10 | 0.400077 | 0.039587 | 0.662053 | 1.789185 | 0.039795 | 0.041107 | 0.107544 | 0.102967 | 0.066239 | 0.715812 | -33.794696 | 78.918522 | 0.335927 |
| 10 | 11 | 0.500000 | 0.037066 | 0.577368 | 1.547009 | 0.034704 | 0.038291 | 0.092987 | 0.090042 | 0.057692 | 0.773504 | -42.263200 | 54.700855 | 0.290995 |
| 11 | 12 | 0.600051 | 0.034873 | 0.747479 | 1.413697 | 0.044929 | 0.035979 | 0.084974 | 0.081028 | 0.074786 | 0.848291 | -25.252076 | 41.369662 | 0.264115 |
| 12 | 13 | 0.699974 | 0.032544 | 0.406296 | 1.269888 | 0.024422 | 0.033713 | 0.076330 | 0.074273 | 0.040598 | 0.888889 | -59.370400 | 26.988787 | 0.200996 |
| 13 | 14 | 0.800026 | 0.030000 | 0.491201 | 1.172505 | 0.029525 | 0.031342 | 0.070477 | 0.068904 | 0.049145 | 0.938034 | -50.879936 | 17.250509 | 0.146834 |
| 14 | 15 | 0.899949 | 0.026507 | 0.342144 | 1.080309 | 0.020566 | 0.028384 | 0.064935 | 0.064405 | 0.034188 | 0.972222 | -65.785600 | 8.030858 | 0.076896 |
| 15 | 16 | 1.000000 | 0.001859 | 0.277635 | 1.000000 | 0.016688 | 0.022058 | 0.060108 | 0.060169 | 0.027778 | 1.000000 | -72.236486 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9343709 | 0.023243433 | 0.9076923 | 0.9230769 | 0.96153843 | 0.88461536 | 0.9692308 | 0.9076923 | 0.95384616 | 0.9076923 | 0.93846154 | 0.9346154 | 0.95384616 | 0.9653846 | 0.9423077 | 0.95384616 | 0.9423077 | 0.8923077 | 0.96525097 | 0.93050194 | 0.95752895 | 0.9498069 | 0.93822396 | 0.9498069 | 0.8918919 | 0.93436295 | 0.90733594 | 0.93822396 | 0.9459459 | 0.9227799 | 0.93822396 | 0.9227799 |
| 1 | auc | 0.764771 | 0.074451886 | 0.7101016 | 0.84991527 | 0.77088434 | 0.69674015 | 0.826 | 0.6549693 | 0.837108 | 0.7139578 | 0.71909064 | 0.74558216 | 0.622 | 0.8848 | 0.83376026 | 0.8301742 | 0.7761324 | 0.8074913 | 0.8422222 | 0.72568166 | 0.8073796 | 0.87263006 | 0.7728909 | 0.7525523 | 0.7217108 | 0.7787172 | 0.7100418 | 0.77021855 | 0.7312243 | 0.5596708 | 0.7941176 | 0.8253644 |
| 2 | err | 0.06562915 | 0.023243433 | 0.092307694 | 0.07692308 | 0.03846154 | 0.115384616 | 0.03076923 | 0.092307694 | 0.046153847 | 0.092307694 | 0.06153846 | 0.06538462 | 0.046153847 | 0.034615386 | 0.057692308 | 0.046153847 | 0.057692308 | 0.10769231 | 0.034749035 | 0.06949807 | 0.042471044 | 0.05019305 | 0.06177606 | 0.05019305 | 0.10810811 | 0.06563707 | 0.09266409 | 0.06177606 | 0.054054055 | 0.077220075 | 0.06177606 | 0.077220075 |
| 3 | err_count | 17.033333 | 6.0371456 | 24.0 | 20.0 | 10.0 | 30.0 | 8.0 | 24.0 | 12.0 | 24.0 | 16.0 | 17.0 | 12.0 | 9.0 | 15.0 | 12.0 | 15.0 | 28.0 | 9.0 | 18.0 | 11.0 | 13.0 | 16.0 | 13.0 | 28.0 | 17.0 | 24.0 | 16.0 | 14.0 | 20.0 | 16.0 | 20.0 |
| 4 | f0point5 | 0.4665471 | 0.12126041 | 0.39325842 | 0.48 | 0.6976744 | 0.28688523 | 0.6034483 | 0.33653846 | 0.5645161 | 0.33333334 | 0.3846154 | 0.46153846 | 0.29411766 | 0.5555556 | 0.5147059 | 0.625 | 0.47297296 | 0.22727273 | 0.5263158 | 0.60240966 | 0.58441556 | 0.61728394 | 0.4861111 | 0.6122449 | 0.36363637 | 0.4054054 | 0.38043478 | 0.4477612 | 0.546875 | 0.3125 | 0.5072464 | 0.3723404 |
| 5 | f1 | 0.45169315 | 0.10708587 | 0.36842105 | 0.54545456 | 0.54545456 | 0.3181818 | 0.6363636 | 0.36842105 | 0.53846157 | 0.33333334 | 0.3846154 | 0.41379312 | 0.25 | 0.5714286 | 0.4827586 | 0.53846157 | 0.4827586 | 0.2631579 | 0.5714286 | 0.5263158 | 0.62068963 | 0.6060606 | 0.46666667 | 0.48 | 0.36363637 | 0.41379312 | 0.36842105 | 0.42857143 | 0.5 | 0.2857143 | 0.46666667 | 0.4117647 |
| 6 | f2 | 0.44575527 | 0.114367954 | 0.34653464 | 0.6315789 | 0.4477612 | 0.35714287 | 0.6730769 | 0.40697673 | 0.5147059 | 0.33333334 | 0.3846154 | 0.375 | 0.2173913 | 0.5882353 | 0.45454547 | 0.47297296 | 0.49295774 | 0.3125 | 0.625 | 0.46728972 | 0.6617647 | 0.5952381 | 0.44871795 | 0.39473686 | 0.36363637 | 0.4225352 | 0.35714287 | 0.41095892 | 0.46052632 | 0.2631579 | 0.43209878 | 0.46052632 |
| 7 | lift_top_group | 7.8378496 | 5.1867685 | 8.253968 | 5.098039 | 17.333334 | 0.0 | 17.333334 | 0.0 | 6.1904764 | 4.814815 | 6.6666665 | 5.098039 | 0.0 | 0.0 | 10.833333 | 5.4166665 | 6.1904764 | 6.1904764 | 19.185184 | 11.26087 | 13.282051 | 15.235294 | 5.3958335 | 10.156863 | 11.772727 | 6.1666665 | 4.3166666 | 11.511111 | 5.3958335 | 10.791667 | 5.0784316 | 6.1666665 |
| 8 | logloss | 0.18740854 | 0.039645713 | 0.25277892 | 0.17737357 | 0.16759875 | 0.23402742 | 0.11354143 | 0.20638484 | 0.15961136 | 0.22940229 | 0.17616738 | 0.21271245 | 0.15857954 | 0.122905925 | 0.17222258 | 0.1690361 | 0.16625899 | 0.1931869 | 0.114648424 | 0.24645974 | 0.13836063 | 0.1629194 | 0.18975163 | 0.20342298 | 0.26575056 | 0.17597124 | 0.24820165 | 0.18392971 | 0.19228755 | 0.21996662 | 0.19773228 | 0.17106514 |
| 9 | max_per_class_error | 0.5539152 | 0.12769444 | 0.6666667 | 0.29411766 | 0.6 | 0.6111111 | 0.3 | 0.5625 | 0.5 | 0.6666667 | 0.61538464 | 0.64705884 | 0.8 | 0.4 | 0.5625 | 0.5625 | 0.5 | 0.64285713 | 0.33333334 | 0.5652174 | 0.30769232 | 0.4117647 | 0.5625 | 0.64705884 | 0.6363636 | 0.5714286 | 0.65 | 0.6 | 0.5625 | 0.75 | 0.5882353 | 0.5 |
| 10 | mcc | 0.42425808 | 0.11882375 | 0.3212981 | 0.5219843 | 0.5703285 | 0.26262626 | 0.62325025 | 0.3246918 | 0.51601464 | 0.28374657 | 0.35222673 | 0.38673693 | 0.23566514 | 0.5541176 | 0.45529196 | 0.53135306 | 0.452549 | 0.21826486 | 0.5599354 | 0.50368136 | 0.6020709 | 0.5795864 | 0.43512467 | 0.49335808 | 0.30456463 | 0.37932518 | 0.31908536 | 0.39722785 | 0.47750816 | 0.24862237 | 0.43890616 | 0.37860048 |
| 11 | mean_per_class_accuracy | 0.7061315 | 0.065251656 | 0.6457462 | 0.822077 | 0.6979592 | 0.65518826 | 0.84 | 0.6880123 | 0.7398374 | 0.6418733 | 0.67611337 | 0.6641249 | 0.592 | 0.79 | 0.70645493 | 0.71260244 | 0.73373985 | 0.63995355 | 0.82133335 | 0.7067981 | 0.8319262 | 0.78172094 | 0.7043467 | 0.67233837 | 0.6522823 | 0.6959184 | 0.65198743 | 0.6856557 | 0.70846194 | 0.6085391 | 0.6934857 | 0.7234694 |
| 12 | mean_per_class_error | 0.29386845 | 0.065251656 | 0.35425383 | 0.17792302 | 0.30204082 | 0.34481177 | 0.16 | 0.3119877 | 0.2601626 | 0.35812673 | 0.32388663 | 0.3358751 | 0.408 | 0.21 | 0.29354507 | 0.28739753 | 0.26626018 | 0.36004645 | 0.17866667 | 0.29320192 | 0.1680738 | 0.21827905 | 0.29565328 | 0.32766163 | 0.34771767 | 0.30408162 | 0.34801257 | 0.31434426 | 0.29153806 | 0.3914609 | 0.30651435 | 0.27653062 |
| 13 | mse | 0.0481346 | 0.011281468 | 0.06563697 | 0.048914846 | 0.041569043 | 0.06193339 | 0.026466863 | 0.053353913 | 0.0415355 | 0.06013307 | 0.04346217 | 0.054830354 | 0.03668766 | 0.029554348 | 0.04500351 | 0.044087388 | 0.042173672 | 0.049912643 | 0.027207537 | 0.06451841 | 0.03406833 | 0.042147428 | 0.04837812 | 0.05269717 | 0.070223376 | 0.04487457 | 0.06583264 | 0.046823744 | 0.049652915 | 0.055510033 | 0.052103195 | 0.044745103 |
| 14 | null_deviance | 118.01836 | 18.57593 | 147.85797 | 125.73113 | 114.7255 | 131.24835 | 87.37807 | 120.223526 | 109.23703 | 131.24835 | 103.75808 | 125.73113 | 87.37807 | 87.37807 | 120.223526 | 120.223526 | 109.23703 | 109.23703 | 81.80913 | 158.85994 | 103.63261 | 125.607956 | 120.09978 | 125.607956 | 153.29364 | 109.11213 | 142.19029 | 114.60118 | 120.09978 | 120.09978 | 125.607956 | 109.11213 |
| 15 | pr_auc | 0.31151608 | 0.120203234 | 0.33363888 | 0.33679542 | 0.4696283 | 0.14650083 | 0.53405035 | 0.14786476 | 0.3121813 | 0.17172736 | 0.20541619 | 0.24513371 | 0.08740308 | 0.32942316 | 0.44890007 | 0.4239356 | 0.31244063 | 0.23596983 | 0.33991212 | 0.48408955 | 0.42181748 | 0.57798684 | 0.32060802 | 0.38693756 | 0.3496755 | 0.20756437 | 0.21714585 | 0.28437024 | 0.2764025 | 0.16111921 | 0.2920128 | 0.28483084 |
| 16 | precision | 0.48336628 | 0.14853878 | 0.4117647 | 0.44444445 | 0.85714287 | 0.26923078 | 0.5833333 | 0.3181818 | 0.5833333 | 0.33333334 | 0.3846154 | 0.5 | 0.33333334 | 0.54545456 | 0.53846157 | 0.7 | 0.46666667 | 0.20833333 | 0.5 | 0.6666667 | 0.5625 | 0.625 | 0.5 | 0.75 | 0.36363637 | 0.4 | 0.3888889 | 0.46153846 | 0.5833333 | 0.33333334 | 0.53846157 | 0.35 |
| 17 | r2 | 0.14805481 | 0.079795055 | 0.11594752 | 0.19955371 | 0.23535585 | 0.038866587 | 0.28433603 | 0.07614641 | 0.18472713 | 0.06680546 | 0.08500695 | 0.10275191 | 0.007965694 | 0.20085043 | 0.22073837 | 0.2366016 | 0.17220083 | 0.020297697 | 0.18884055 | 0.20266043 | 0.28538528 | 0.31276333 | 0.16531564 | 0.14074409 | 0.09653731 | 0.12238158 | 0.076125674 | 0.1418078 | 0.1433212 | 0.042266347 | 0.15042913 | 0.12491365 |
| 18 | recall | 0.44608477 | 0.12769444 | 0.33333334 | 0.7058824 | 0.4 | 0.3888889 | 0.7 | 0.4375 | 0.5 | 0.33333334 | 0.3846154 | 0.3529412 | 0.2 | 0.6 | 0.4375 | 0.4375 | 0.5 | 0.35714287 | 0.6666667 | 0.4347826 | 0.6923077 | 0.5882353 | 0.4375 | 0.3529412 | 0.36363637 | 0.42857143 | 0.35 | 0.4 | 0.4375 | 0.25 | 0.4117647 | 0.5 |
| 19 | residual_deviance | 97.271736 | 20.547543 | 131.44504 | 92.23425 | 87.15135 | 121.69425 | 59.041546 | 107.320114 | 82.99791 | 119.28919 | 91.60704 | 110.61047 | 82.46136 | 63.911083 | 89.55574 | 87.89877 | 86.45467 | 100.457184 | 59.387886 | 127.666145 | 71.67081 | 84.39225 | 98.29134 | 105.37311 | 137.65878 | 91.15311 | 128.56845 | 95.27559 | 99.60495 | 113.9427 | 102.42532 | 88.61174 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:16:20 | 0.000 sec | 2 | .85E1 | 15.0 | 0.452159 | 0.451812 | 0.452501 | 0.013073 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:16:20 | 0.003 sec | 4 | .53E1 | 15.0 | 0.450748 | 0.450245 | 0.451152 | 0.013055 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:16:20 | 0.005 sec | 6 | .33E1 | 15.0 | 0.448529 | 0.447781 | 0.44903 | 0.013027 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:16:20 | 0.008 sec | 8 | .2E1 | 15.0 | 0.44508 | 0.443951 | 0.445726 | 0.012986 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:16:20 | 0.013 sec | 10 | .13E1 | 15.0 | 0.439879 | 0.438178 | 0.440735 | 0.012934 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:16:20 | 0.018 sec | 12 | .79E0 | 15.0 | 0.43238 | 0.429859 | 0.433512 | 0.012878 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:16:20 | 0.020 sec | 14 | .49E0 | 15.0 | 0.422384 | 0.41878 | 0.423824 | 0.012848 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:16:20 | 0.028 sec | 16 | .3E0 | 15.0 | 0.410679 | 0.40583 | 0.412376 | 0.012895 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:16:20 | 0.032 sec | 18 | .19E0 | 15.0 | 0.399193 | 0.393158 | 0.401033 | 0.013066 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:16:20 | 0.034 sec | 20 | .12E0 | 15.0 | 0.389888 | 0.38291 | 0.391809 | 0.013348 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:16:20 | 0.037 sec | 22 | .73E-1 | 15.0 | 0.383436 | 0.37578 | 0.385467 | 0.013672 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:16:20 | 0.040 sec | 24 | .45E-1 | 15.0 | 0.379397 | 0.371232 | 0.38161 | 0.013965 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:16:20 | 0.042 sec | 26 | .28E-1 | 15.0 | 0.376995 | 0.368394 | 0.379449 | 0.01419 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:16:20 | 0.045 sec | 28 | .17E-1 | 15.0 | 0.375583 | 0.366576 | 0.378302 | 0.014347 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:16:20 | 0.300 sec | 29 | None | NaN | 29.0 | 0.219618 | 0.187378 | 0.14626 | 0.768614 | 0.291468 | 9.171543 | 0.076548 | 0.219056 | 0.182685 | 0.150416 | 0.80163 | 0.291321 | 5.824359 | 0.06831 | ||||||
| 15 | 2021-07-15 20:16:20 | 0.048 sec | 30 | .11E-1 | 15.0 | 0.374755 | 0.365371 | 0.377732 | 0.01445 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:16:20 | 0.050 sec | 32 | .67E-2 | 15.0 | 0.374274 | 0.364557 | 0.378062 | 0.014623 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:16:20 | 0.053 sec | 34 | .42E-2 | 15.0 | 0.373999 | 0.364012 | 0.379362 | 0.014873 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:16:20 | 0.054 sec | 35 | .26E-2 | 15.0 | 0.373848 | 0.363662 | 0.383186 | 0.015348 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.545912 | 1.000000 | 0.285704 |
| 1 | Average_Transaction_Frequency | 0.213814 | 0.391664 | 0.111900 |
| 2 | Merchant_ID | 0.197896 | 0.362505 | 0.103569 |
| 3 | Minimum_Transaction_Amount | 0.182063 | 0.333502 | 0.095283 |
| 4 | Channel_ID | 0.164423 | 0.301189 | 0.086051 |
| 5 | Transaction_Amount | 0.119864 | 0.219566 | 0.062731 |
| 6 | Card_Type.1 | 0.118889 | 0.217780 | 0.062221 |
| 7 | Card_Type.0 | 0.117503 | 0.215242 | 0.061495 |
| 8 | Transaction_Date | 0.074224 | 0.135963 | 0.038845 |
| 9 | Maximum_Transaction_Amount | 0.064640 | 0.118407 | 0.033829 |
| 10 | Month | 0.038839 | 0.071145 | 0.020326 |
| 11 | Average_Transaction_Amount | 0.034995 | 0.064103 | 0.018315 |
| 12 | Day | 0.031953 | 0.058531 | 0.016723 |
| 13 | City_ID | 0.005748 | 0.010528 | 0.003008 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201623 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.006651 ) | nlambda = 30, lambda.max = 8.4403, lambda.min = 0.006651, lambda.1... | 14 | 14 | 32 | automl_training_py_845_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04857331091774932 RMSE: 0.22039353646999116 LogLoss: 0.18901736254474094 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938366 Residual deviance: 2943.378369546707 AIC: 2973.378369546707 AUC: 0.757967124734001 AUCPR: 0.28404301609856625 Gini: 0.5159342494680019 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.24079134026875595:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7016.0 | 302.0 | 0.0413 | (302.0/7318.0) |
| 1 | 1 | 276.0 | 192.0 | 0.5897 | (276.0/468.0) |
| 2 | Total | 7292.0 | 494.0 | 0.0742 | (578.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.240791 | 0.399168 | 151.0 |
| 1 | max f2 | 0.068004 | 0.423630 | 221.0 |
| 2 | max f0point5 | 0.343335 | 0.409292 | 102.0 |
| 3 | max accuracy | 0.443126 | 0.941433 | 33.0 |
| 4 | max precision | 0.799777 | 1.000000 | 0.0 |
| 5 | max recall | 0.018191 | 1.000000 | 384.0 |
| 6 | max specificity | 0.799777 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.240791 | 0.359787 | 151.0 |
| 8 | max min_per_class_accuracy | 0.042113 | 0.681060 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.049666 | 0.702552 | 256.0 |
| 10 | max tns | 0.799777 | 7318.000000 | 0.0 |
| 11 | max fns | 0.799777 | 467.000000 | 0.0 |
| 12 | max fps | 0.002395 | 7318.000000 | 399.0 |
| 13 | max tps | 0.018191 | 468.000000 | 384.0 |
| 14 | max tnr | 0.799777 | 1.000000 | 0.0 |
| 15 | max fnr | 0.799777 | 0.997863 | 0.0 |
| 16 | max fpr | 0.002395 | 1.000000 | 399.0 |
| 17 | max tpr | 0.018191 | 1.000000 | 384.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.422635 | 9.384835 | 9.384835 | 0.564103 | 0.485228 | 0.564103 | 0.485228 | 0.094017 | 0.094017 | 838.483454 | 838.483454 | 0.089371 |
| 1 | 2 | 0.020036 | 0.393422 | 6.185459 | 7.785147 | 0.371795 | 0.407011 | 0.467949 | 0.446120 | 0.061966 | 0.155983 | 518.545913 | 678.514683 | 0.144641 |
| 2 | 3 | 0.030054 | 0.370210 | 7.465209 | 7.678501 | 0.448718 | 0.381068 | 0.461538 | 0.424436 | 0.074786 | 0.230769 | 646.520929 | 667.850099 | 0.213551 |
| 3 | 4 | 0.040072 | 0.347832 | 6.825334 | 7.465209 | 0.410256 | 0.357055 | 0.448718 | 0.407590 | 0.068376 | 0.299145 | 582.533421 | 646.520929 | 0.275642 |
| 4 | 5 | 0.050090 | 0.325143 | 5.119001 | 6.995968 | 0.307692 | 0.336481 | 0.420513 | 0.393368 | 0.051282 | 0.350427 | 411.900066 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.062497 | 2.437776 | 4.719797 | 0.146530 | 0.149034 | 0.283697 | 0.271358 | 0.121795 | 0.472222 | 143.777602 | 371.979746 | 0.395972 |
| 6 | 7 | 0.150013 | 0.052046 | 0.940896 | 3.461242 | 0.056555 | 0.056130 | 0.208048 | 0.199677 | 0.047009 | 0.519231 | -5.910399 | 246.124210 | 0.392830 |
| 7 | 8 | 0.200103 | 0.047685 | 1.066458 | 2.861778 | 0.064103 | 0.049623 | 0.172015 | 0.162115 | 0.053419 | 0.572650 | 6.645847 | 186.177765 | 0.396372 |
| 8 | 9 | 0.300026 | 0.043445 | 0.855360 | 2.193544 | 0.051414 | 0.045438 | 0.131849 | 0.123256 | 0.085470 | 0.658120 | -14.463999 | 119.354437 | 0.380995 |
| 9 | 10 | 0.400077 | 0.040239 | 0.555270 | 1.783844 | 0.033376 | 0.041754 | 0.107223 | 0.102874 | 0.055556 | 0.713675 | -44.472971 | 78.384437 | 0.333653 |
| 10 | 11 | 0.500000 | 0.037704 | 0.705672 | 1.568376 | 0.042416 | 0.038982 | 0.094272 | 0.090105 | 0.070513 | 0.784188 | -29.432799 | 56.837607 | 0.302362 |
| 11 | 12 | 0.600051 | 0.035146 | 0.576627 | 1.403014 | 0.034660 | 0.036383 | 0.084332 | 0.081148 | 0.057692 | 0.841880 | -42.337316 | 40.301377 | 0.257294 |
| 12 | 13 | 0.699974 | 0.032577 | 0.513216 | 1.275993 | 0.030848 | 0.033912 | 0.076697 | 0.074405 | 0.051282 | 0.893162 | -48.678400 | 27.599310 | 0.205543 |
| 13 | 14 | 0.800026 | 0.029681 | 0.448488 | 1.172505 | 0.026958 | 0.031178 | 0.070477 | 0.068999 | 0.044872 | 0.938034 | -55.151246 | 17.250509 | 0.146834 |
| 14 | 15 | 0.899949 | 0.025588 | 0.363528 | 1.082683 | 0.021851 | 0.027812 | 0.065078 | 0.064426 | 0.036325 | 0.974359 | -63.647200 | 8.268288 | 0.079169 |
| 15 | 16 | 1.000000 | 0.002068 | 0.256279 | 1.000000 | 0.015404 | 0.021269 | 0.060108 | 0.060108 | 0.025641 | 1.000000 | -74.372140 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04612702996605346 RMSE: 0.21477204186311927 LogLoss: 0.17584335637691395 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311391 Residual deviance: 684.7340297317029 AIC: 714.7340297317029 AUC: 0.8186586334127317 AUCPR: 0.33683360161410314 Gini: 0.6373172668254634 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.1286566409367437:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1737.0 | 93.0 | 0.0508 | (93.0/1830.0) |
| 1 | 1 | 54.0 | 63.0 | 0.4615 | (54.0/117.0) |
| 2 | Total | 1791.0 | 156.0 | 0.0755 | (147.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.128657 | 0.461538 | 131.0 |
| 1 | max f2 | 0.062001 | 0.530523 | 172.0 |
| 2 | max f0point5 | 0.328928 | 0.448155 | 92.0 |
| 3 | max accuracy | 0.531531 | 0.941448 | 3.0 |
| 4 | max precision | 0.619928 | 1.000000 | 0.0 |
| 5 | max recall | 0.023340 | 1.000000 | 359.0 |
| 6 | max specificity | 0.619928 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.128657 | 0.426883 | 131.0 |
| 8 | max min_per_class_accuracy | 0.043584 | 0.719672 | 236.0 |
| 9 | max mean_per_class_accuracy | 0.055965 | 0.775333 | 186.0 |
| 10 | max tns | 0.619928 | 1830.000000 | 0.0 |
| 11 | max fns | 0.619928 | 116.000000 | 0.0 |
| 12 | max fps | 0.002158 | 1830.000000 | 399.0 |
| 13 | max tps | 0.023340 | 117.000000 | 359.0 |
| 14 | max tnr | 0.619928 | 1.000000 | 0.0 |
| 15 | max fnr | 0.619928 | 0.991453 | 0.0 |
| 16 | max fpr | 0.002158 | 1.000000 | 399.0 |
| 17 | max tpr | 0.023340 | 1.000000 | 359.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.40 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.453939 | 7.488462 | 7.488462 | 0.450000 | 0.501528 | 0.450000 | 0.501528 | 0.076923 | 0.076923 | 648.846154 | 648.846154 | 0.070912 |
| 1 | 2 | 0.020031 | 0.407522 | 7.882591 | 7.680473 | 0.473684 | 0.425906 | 0.461538 | 0.464687 | 0.076923 | 0.153846 | 688.259109 | 668.047337 | 0.142371 |
| 2 | 3 | 0.030303 | 0.385112 | 7.488462 | 7.615385 | 0.450000 | 0.395289 | 0.457627 | 0.441162 | 0.076923 | 0.230769 | 648.846154 | 661.538462 | 0.213283 |
| 3 | 4 | 0.040062 | 0.364206 | 7.882591 | 7.680473 | 0.473684 | 0.374124 | 0.461538 | 0.424832 | 0.076923 | 0.307692 | 688.259109 | 668.047337 | 0.284741 |
| 4 | 5 | 0.050334 | 0.345039 | 7.488462 | 7.641287 | 0.450000 | 0.356386 | 0.459184 | 0.410864 | 0.076923 | 0.384615 | 648.846154 | 664.128728 | 0.355654 |
| 5 | 6 | 0.100154 | 0.068812 | 4.117367 | 5.888363 | 0.247423 | 0.203904 | 0.353846 | 0.307915 | 0.205128 | 0.589744 | 311.736717 | 488.836292 | 0.520891 |
| 6 | 7 | 0.149974 | 0.052908 | 1.200899 | 4.331226 | 0.072165 | 0.058680 | 0.260274 | 0.225121 | 0.059829 | 0.649573 | 20.089876 | 333.122585 | 0.531540 |
| 7 | 8 | 0.200308 | 0.048450 | 0.339613 | 3.328205 | 0.020408 | 0.050430 | 0.200000 | 0.181224 | 0.017094 | 0.666667 | -66.038723 | 232.820513 | 0.496175 |
| 8 | 9 | 0.299949 | 0.043739 | 0.514671 | 2.393572 | 0.030928 | 0.045804 | 0.143836 | 0.136239 | 0.051282 | 0.717949 | -48.532910 | 139.357218 | 0.444725 |
| 9 | 10 | 0.400103 | 0.040410 | 0.938725 | 2.029393 | 0.056410 | 0.042092 | 0.121951 | 0.112672 | 0.094017 | 0.811966 | -6.127548 | 102.939337 | 0.438195 |
| 10 | 11 | 0.500257 | 0.037799 | 0.426693 | 1.708524 | 0.025641 | 0.039118 | 0.102669 | 0.097946 | 0.042735 | 0.854701 | -57.330703 | 70.852419 | 0.377105 |
| 11 | 12 | 0.599897 | 0.035330 | 0.428892 | 1.495983 | 0.025773 | 0.036491 | 0.089897 | 0.087739 | 0.042735 | 0.897436 | -57.110759 | 49.598261 | 0.316562 |
| 12 | 13 | 0.700051 | 0.032735 | 0.426693 | 1.343003 | 0.025641 | 0.034024 | 0.080704 | 0.080054 | 0.042735 | 0.940171 | -57.330703 | 34.300280 | 0.255471 |
| 13 | 14 | 0.799692 | 0.029635 | 0.343114 | 1.218418 | 0.020619 | 0.031315 | 0.073218 | 0.073981 | 0.034188 | 0.974359 | -65.688607 | 21.841806 | 0.185834 |
| 14 | 15 | 0.899846 | 0.025028 | 0.085339 | 1.092305 | 0.005128 | 0.027501 | 0.065639 | 0.068808 | 0.008547 | 0.982906 | -91.466141 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.002105 | 0.170677 | 1.000000 | 0.010256 | 0.020575 | 0.060092 | 0.063977 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04887241408166055 RMSE: 0.22107106115830844 LogLoss: 0.1904822586622831 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.388287283011 Residual deviance: 2966.1897318890724 AIC: 2996.1897318890724 AUC: 0.7494717976748586 AUCPR: 0.26701738490647453 Gini: 0.4989435953497172 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2200464008017094:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7030.0 | 288.0 | 0.0394 | (288.0/7318.0) |
| 1 | 1 | 281.0 | 187.0 | 0.6004 | (281.0/468.0) |
| 2 | Total | 7311.0 | 475.0 | 0.0731 | (569.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.220046 | 0.396607 | 163.0 |
| 1 | max f2 | 0.068073 | 0.410551 | 221.0 |
| 2 | max f0point5 | 0.323798 | 0.406379 | 117.0 |
| 3 | max accuracy | 0.437105 | 0.940920 | 37.0 |
| 4 | max precision | 0.564325 | 0.625000 | 7.0 |
| 5 | max recall | 0.017440 | 1.000000 | 388.0 |
| 6 | max specificity | 0.816837 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.220046 | 0.357725 | 163.0 |
| 8 | max min_per_class_accuracy | 0.043674 | 0.673077 | 281.0 |
| 9 | max mean_per_class_accuracy | 0.051035 | 0.695099 | 254.0 |
| 10 | max tns | 0.816837 | 7317.000000 | 0.0 |
| 11 | max fns | 0.816837 | 468.000000 | 0.0 |
| 12 | max fps | 0.002510 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017440 | 468.000000 | 388.0 |
| 14 | max tnr | 0.816837 | 0.999863 | 0.0 |
| 15 | max fnr | 0.816837 | 1.000000 | 0.0 |
| 16 | max fpr | 0.002510 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017440 | 1.000000 | 388.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.04 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.419366 | 8.531668 | 8.531668 | 0.512821 | 0.485590 | 0.512821 | 0.485590 | 0.085470 | 0.085470 | 753.166776 | 753.166776 | 0.080277 |
| 1 | 2 | 0.020036 | 0.390869 | 5.972167 | 7.251918 | 0.358974 | 0.405006 | 0.435897 | 0.445298 | 0.059829 | 0.145299 | 497.216743 | 625.191760 | 0.133274 |
| 2 | 3 | 0.030054 | 0.364035 | 7.678501 | 7.394112 | 0.461538 | 0.377810 | 0.444444 | 0.422802 | 0.076923 | 0.222222 | 667.850099 | 639.411206 | 0.204458 |
| 3 | 4 | 0.040072 | 0.343283 | 7.038626 | 7.305241 | 0.423077 | 0.352673 | 0.439103 | 0.405270 | 0.070513 | 0.292735 | 603.862590 | 630.524052 | 0.268821 |
| 4 | 5 | 0.050090 | 0.313741 | 5.758876 | 6.995968 | 0.346154 | 0.328881 | 0.420513 | 0.389992 | 0.057692 | 0.350427 | 475.887574 | 599.596757 | 0.319545 |
| 5 | 6 | 0.132802 | 0.060058 | 1.653342 | 3.668452 | 0.099379 | 0.100739 | 0.220503 | 0.209838 | 0.136752 | 0.487179 | 65.334183 | 266.845212 | 0.377040 |
| 6 | 7 | 0.166966 | 0.059793 | 1.125795 | 3.148185 | 0.067669 | 0.059795 | 0.189231 | 0.179137 | 0.038462 | 0.525641 | 12.579526 | 214.818540 | 0.381613 |
| 7 | 8 | 0.200103 | 0.053264 | 0.838286 | 2.765673 | 0.050388 | 0.056094 | 0.166239 | 0.158762 | 0.027778 | 0.553419 | -16.171404 | 176.567317 | 0.375911 |
| 8 | 9 | 0.300026 | 0.045468 | 0.962280 | 2.165057 | 0.057841 | 0.048510 | 0.130137 | 0.122043 | 0.096154 | 0.649573 | -3.771999 | 116.505678 | 0.371901 |
| 9 | 10 | 0.400077 | 0.041647 | 0.704766 | 1.799867 | 0.042362 | 0.043487 | 0.108186 | 0.102397 | 0.070513 | 0.720085 | -29.523386 | 79.986692 | 0.340474 |
| 10 | 11 | 0.500000 | 0.038708 | 0.449064 | 1.529915 | 0.026992 | 0.040110 | 0.091960 | 0.089950 | 0.044872 | 0.764957 | -55.093600 | 52.991453 | 0.281902 |
| 11 | 12 | 0.600051 | 0.035972 | 0.704766 | 1.392331 | 0.042362 | 0.037341 | 0.083690 | 0.081178 | 0.070513 | 0.835470 | -29.523386 | 39.233093 | 0.250474 |
| 12 | 13 | 0.699974 | 0.033220 | 0.534600 | 1.269888 | 0.032134 | 0.034653 | 0.076330 | 0.074536 | 0.053419 | 0.888889 | -46.540000 | 26.988787 | 0.200996 |
| 13 | 14 | 0.800026 | 0.030223 | 0.427131 | 1.164493 | 0.025674 | 0.031782 | 0.069995 | 0.069189 | 0.042735 | 0.931624 | -57.286901 | 16.449252 | 0.140014 |
| 14 | 15 | 0.899949 | 0.026169 | 0.384912 | 1.077934 | 0.023136 | 0.028362 | 0.064792 | 0.064656 | 0.038462 | 0.970085 | -61.508800 | 7.793428 | 0.074622 |
| 15 | 16 | 1.000000 | 0.002104 | 0.298992 | 1.000000 | 0.017972 | 0.021655 | 0.060108 | 0.060354 | 0.029915 | 1.000000 | -70.100831 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93065935 | 0.027111866 | 0.9423077 | 0.91923076 | 0.95384616 | 0.89615387 | 0.91923076 | 0.93846154 | 0.9346154 | 0.8384615 | 0.9153846 | 0.9307692 | 0.95384616 | 0.90384614 | 0.95 | 0.9461538 | 0.88461536 | 0.95 | 0.9459459 | 0.969112 | 0.94208497 | 0.9189189 | 0.93050194 | 0.93436295 | 0.9227799 | 0.9459459 | 0.9034749 | 0.969112 | 0.9498069 | 0.9111969 | 0.9459459 | 0.95366794 |
| 1 | auc | 0.7438192 | 0.07003453 | 0.7968989 | 0.82086664 | 0.87482995 | 0.598944 | 0.6508709 | 0.7123463 | 0.83809525 | 0.696209 | 0.7763832 | 0.7772936 | 0.7811668 | 0.73129994 | 0.7590726 | 0.7174693 | 0.69886225 | 0.7356572 | 0.6928962 | 0.6377551 | 0.74831307 | 0.6916152 | 0.7320084 | 0.7066733 | 0.78054404 | 0.85200554 | 0.87280333 | 0.6311111 | 0.8167234 | 0.7498542 | 0.7004386 | 0.7355685 |
| 2 | err | 0.06934066 | 0.027111866 | 0.057692308 | 0.08076923 | 0.046153847 | 0.103846155 | 0.08076923 | 0.06153846 | 0.06538462 | 0.16153847 | 0.08461539 | 0.06923077 | 0.046153847 | 0.09615385 | 0.05 | 0.053846154 | 0.115384616 | 0.05 | 0.054054055 | 0.03088803 | 0.057915058 | 0.08108108 | 0.06949807 | 0.06563707 | 0.077220075 | 0.054054055 | 0.096525095 | 0.03088803 | 0.05019305 | 0.08880309 | 0.054054055 | 0.046332046 |
| 3 | err_count | 18.0 | 7.0515347 | 15.0 | 21.0 | 12.0 | 27.0 | 21.0 | 16.0 | 17.0 | 42.0 | 22.0 | 18.0 | 12.0 | 25.0 | 13.0 | 14.0 | 30.0 | 13.0 | 14.0 | 8.0 | 15.0 | 21.0 | 18.0 | 17.0 | 20.0 | 14.0 | 25.0 | 8.0 | 13.0 | 23.0 | 14.0 | 12.0 |
| 4 | f0point5 | 0.4507586 | 0.13558723 | 0.6 | 0.45454547 | 0.59322035 | 0.23255815 | 0.25 | 0.5 | 0.3968254 | 0.2173913 | 0.36458334 | 0.45454547 | 0.6603774 | 0.3539823 | 0.46875 | 0.5208333 | 0.28 | 0.6122449 | 0.53333336 | 0.2631579 | 0.41666666 | 0.32894737 | 0.54347825 | 0.2631579 | 0.45918366 | 0.6122449 | 0.47619048 | 0.5405405 | 0.61403507 | 0.3301887 | 0.63829786 | 0.54347825 |
| 5 | f1 | 0.4322945 | 0.11045967 | 0.54545456 | 0.5116279 | 0.53846157 | 0.22857143 | 0.22222222 | 0.5 | 0.37037036 | 0.27586207 | 0.3888889 | 0.4375 | 0.53846157 | 0.3902439 | 0.48 | 0.41666666 | 0.3181818 | 0.48 | 0.53333336 | 0.2 | 0.44444445 | 0.32258064 | 0.5263158 | 0.32 | 0.47368422 | 0.6315789 | 0.5614035 | 0.5 | 0.5185185 | 0.3783784 | 0.46153846 | 0.45454547 |
| 6 | f2 | 0.4298958 | 0.1158385 | 0.5 | 0.5851064 | 0.49295774 | 0.2247191 | 0.2 | 0.5 | 0.3472222 | 0.3773585 | 0.41666666 | 0.42168674 | 0.45454547 | 0.4347826 | 0.4918033 | 0.3472222 | 0.36842105 | 0.39473686 | 0.53333336 | 0.16129032 | 0.47619048 | 0.3164557 | 0.5102041 | 0.40816328 | 0.48913044 | 0.65217394 | 0.6837607 | 0.4651163 | 0.44871795 | 0.443038 | 0.36144578 | 0.390625 |
| 7 | lift_top_group | 8.865143 | 5.5061803 | 13.684211 | 0.0 | 11.555555 | 4.814815 | 5.4166665 | 10.833333 | 0.0 | 10.833333 | 10.833333 | 0.0 | 10.196078 | 10.196078 | 14.444445 | 16.25 | 0.0 | 15.294118 | 5.7555556 | 12.333333 | 0.0 | 10.791667 | 8.633333 | 0.0 | 9.592592 | 14.388889 | 8.633333 | 19.185184 | 10.156863 | 6.1666665 | 13.631579 | 12.333333 |
| 8 | logloss | 0.18894175 | 0.029992737 | 0.19913138 | 0.19122352 | 0.15549932 | 0.2452234 | 0.22443078 | 0.18870597 | 0.19098897 | 0.22128429 | 0.19631585 | 0.20369191 | 0.18398355 | 0.20777442 | 0.1490524 | 0.20499364 | 0.22877267 | 0.20231901 | 0.17267907 | 0.12972997 | 0.16205566 | 0.20957533 | 0.21635617 | 0.12902792 | 0.2031083 | 0.17150004 | 0.18869331 | 0.12617496 | 0.18380994 | 0.18127638 | 0.22348848 | 0.17738593 |
| 9 | max_per_class_error | 0.5625404 | 0.13650885 | 0.5263158 | 0.3529412 | 0.53333336 | 0.7777778 | 0.8125 | 0.5 | 0.6666667 | 0.5 | 0.5625 | 0.5882353 | 0.5882353 | 0.5294118 | 0.5 | 0.6875 | 0.5882353 | 0.64705884 | 0.46666667 | 0.85714287 | 0.5 | 0.6875 | 0.5 | 0.5 | 0.5 | 0.33333334 | 0.2 | 0.5555556 | 0.5882353 | 0.5 | 0.68421054 | 0.64285713 |
| 10 | mcc | 0.40948972 | 0.117840424 | 0.52226615 | 0.4823187 | 0.5216429 | 0.17303124 | 0.18470813 | 0.46721312 | 0.338667 | 0.23550214 | 0.34650996 | 0.40165952 | 0.5456949 | 0.34565836 | 0.4541826 | 0.41776058 | 0.26697505 | 0.49344316 | 0.5046448 | 0.20447822 | 0.41716427 | 0.27967188 | 0.48971808 | 0.31313276 | 0.43284053 | 0.60347325 | 0.5432482 | 0.48839623 | 0.5133408 | 0.34554982 | 0.5010284 | 0.45077085 |
| 11 | mean_per_class_accuracy | 0.6995691 | 0.06358422 | 0.7264687 | 0.79266524 | 0.7251701 | 0.5842516 | 0.5773566 | 0.7336066 | 0.65238094 | 0.6803279 | 0.69211066 | 0.6894215 | 0.70176715 | 0.7023723 | 0.7358871 | 0.65010244 | 0.6647301 | 0.67235535 | 0.7523224 | 0.5674603 | 0.73178136 | 0.6356739 | 0.7332636 | 0.72410357 | 0.7271784 | 0.8167358 | 0.85606694 | 0.7162222 | 0.699684 | 0.71734697 | 0.6558114 | 0.672449 |
| 12 | mean_per_class_error | 0.30043086 | 0.06358422 | 0.27353135 | 0.20733479 | 0.27482992 | 0.4157484 | 0.42264345 | 0.26639345 | 0.34761906 | 0.31967214 | 0.30788934 | 0.31057855 | 0.29823288 | 0.2976277 | 0.2641129 | 0.34989753 | 0.3352699 | 0.32764465 | 0.2476776 | 0.43253967 | 0.26821864 | 0.36432612 | 0.2667364 | 0.2758964 | 0.27282158 | 0.18326418 | 0.14393306 | 0.28377777 | 0.300316 | 0.28265306 | 0.3441886 | 0.327551 |
| 13 | mse | 0.048356842 | 0.009010416 | 0.052197784 | 0.051219467 | 0.04090376 | 0.06308432 | 0.056681015 | 0.048387744 | 0.050670143 | 0.056204814 | 0.051245824 | 0.05419394 | 0.046894114 | 0.054373853 | 0.03613467 | 0.051809076 | 0.05956954 | 0.051447287 | 0.043222126 | 0.027835872 | 0.041074075 | 0.05329014 | 0.056962762 | 0.029059712 | 0.053745724 | 0.044723332 | 0.052169483 | 0.027475469 | 0.048505373 | 0.04689773 | 0.05632841 | 0.04439773 |
| 14 | null_deviance | 118.01294 | 17.680279 | 136.7752 | 125.73113 | 114.7255 | 131.24835 | 120.223526 | 120.223526 | 114.7255 | 120.223526 | 120.223526 | 125.73113 | 125.73113 | 125.73113 | 98.28862 | 120.223526 | 125.73113 | 125.73113 | 114.60118 | 70.953766 | 98.16257 | 120.09978 | 142.19029 | 76.37677 | 131.12575 | 131.12575 | 142.19029 | 81.80913 | 125.607956 | 109.11213 | 136.65318 | 109.11213 |
| 15 | pr_auc | 0.30860248 | 0.12994224 | 0.50999236 | 0.3117596 | 0.5061905 | 0.12892543 | 0.17151645 | 0.3309745 | 0.22561741 | 0.21478389 | 0.32240844 | 0.2407042 | 0.4319767 | 0.27461427 | 0.34038436 | 0.3615484 | 0.14549841 | 0.391512 | 0.302115 | 0.07124243 | 0.19624765 | 0.2665683 | 0.3929227 | 0.10147373 | 0.30439165 | 0.5759082 | 0.51998854 | 0.36449665 | 0.43978465 | 0.15802132 | 0.37070417 | 0.28580236 |
| 16 | precision | 0.4757413 | 0.17629252 | 0.64285713 | 0.42307693 | 0.6363636 | 0.23529412 | 0.27272728 | 0.5 | 0.41666666 | 0.1904762 | 0.35 | 0.46666667 | 0.7777778 | 0.33333334 | 0.46153846 | 0.625 | 0.25925925 | 0.75 | 0.53333336 | 0.33333334 | 0.4 | 0.33333334 | 0.5555556 | 0.23529412 | 0.45 | 0.6 | 0.43243244 | 0.5714286 | 0.7 | 0.3043478 | 0.85714287 | 0.625 |
| 17 | r2 | 0.13401338 | 0.086780146 | 0.22940157 | 0.16184072 | 0.24759345 | 0.021005485 | 0.018535702 | 0.16213848 | 0.067945115 | 0.02678137 | 0.11264915 | 0.1131662 | 0.23262112 | 0.11022212 | 0.17919904 | 0.10289615 | 0.025199478 | 0.15811267 | 0.20781872 | -0.05853639 | 0.070414975 | 0.080566935 | 0.20060271 | 0.0292059 | 0.16889884 | 0.30841726 | 0.26787007 | 0.18085246 | 0.2090936 | 0.082814336 | 0.17136711 | 0.13170724 |
| 18 | recall | 0.4374596 | 0.13650885 | 0.47368422 | 0.64705884 | 0.46666667 | 0.22222222 | 0.1875 | 0.5 | 0.33333334 | 0.5 | 0.4375 | 0.4117647 | 0.4117647 | 0.47058824 | 0.5 | 0.3125 | 0.4117647 | 0.3529412 | 0.53333336 | 0.14285715 | 0.5 | 0.3125 | 0.5 | 0.5 | 0.5 | 0.6666667 | 0.8 | 0.44444445 | 0.4117647 | 0.5 | 0.31578946 | 0.35714287 |
| 19 | residual_deviance | 98.084724 | 15.636498 | 103.548325 | 99.43623 | 80.85964 | 127.51617 | 116.70401 | 98.127106 | 99.31427 | 115.067825 | 102.08424 | 105.9198 | 95.67144 | 108.0427 | 77.50725 | 106.59669 | 118.96179 | 105.20589 | 89.44776 | 67.20013 | 83.94483 | 108.56002 | 112.072495 | 66.836464 | 105.2101 | 88.83702 | 97.74314 | 65.35863 | 95.21355 | 93.90117 | 115.76703 | 91.88591 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:16:30 | 0.000 sec | 2 | .84E1 | 14 | 0.452254 | 0.451623 | 0.452510 | 0.012321 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:16:30 | 0.003 sec | 4 | .52E1 | 14 | 0.450898 | 0.449941 | 0.451214 | 0.012264 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:16:30 | 0.007 sec | 6 | .33E1 | 14 | 0.448769 | 0.447296 | 0.449177 | 0.012175 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:16:30 | 0.010 sec | 8 | .2E1 | 14 | 0.445458 | 0.443182 | 0.446006 | 0.012041 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:16:30 | 0.013 sec | 10 | .13E1 | 15 | 0.440469 | 0.436973 | 0.441216 | 0.011846 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:16:30 | 0.016 sec | 12 | .78E0 | 15 | 0.433284 | 0.428000 | 0.434288 | 0.011582 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:16:30 | 0.019 sec | 14 | .48E0 | 15 | 0.423720 | 0.415983 | 0.425007 | 0.011269 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:16:30 | 0.023 sec | 16 | .3E0 | 15 | 0.412561 | 0.401786 | 0.414067 | 0.010972 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:16:30 | 0.026 sec | 18 | .19E0 | 15 | 0.401666 | 0.387585 | 0.403274 | 0.010783 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:16:30 | 0.029 sec | 20 | .12E0 | 15 | 0.392894 | 0.375658 | 0.394537 | 0.010740 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:16:30 | 0.033 sec | 22 | .72E-1 | 15 | 0.386834 | 0.366875 | 0.388551 | 0.010802 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:16:30 | 0.036 sec | 24 | .45E-1 | 15 | 0.383031 | 0.360887 | 0.384901 | 0.010905 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:16:30 | 0.040 sec | 26 | .28E-1 | 15 | 0.380741 | 0.356941 | 0.382825 | 0.011005 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:16:30 | 0.043 sec | 28 | .17E-1 | 15 | 0.379364 | 0.354372 | 0.381694 | 0.011084 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:16:30 | 0.046 sec | 30 | .11E-1 | 15 | 0.378533 | 0.352721 | 0.381084 | 0.011138 | 0.0 | 30.0 | 0.220394 | 0.189017 | 0.140218 | 0.757967 | 0.284043 | 9.384835 | 0.074236 | 0.214772 | 0.175843 | 0.183323 | 0.818659 | 0.336834 | 7.488462 | 0.075501 | |
| 15 | 2021-07-15 20:16:30 | 0.050 sec | 32 | .67E-2 | 15 | 0.378035 | 0.351687 | 0.380918 | 0.011227 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:16:30 | 0.053 sec | 34 | .41E-2 | 15 | 0.377744 | 0.351059 | 0.381454 | 0.011437 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:16:30 | 0.056 sec | 35 | .26E-2 | 15 | 0.377584 | 0.350700 | 0.384938 | 0.011353 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:16:30 | 0.058 sec | 36 | .16E-2 | 15 | 0.377496 | 0.350486 | 0.390443 | 0.011898 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.557579 | 1.000000 | 0.272142 |
| 1 | Merchant_ID | 0.206119 | 0.369668 | 0.100602 |
| 2 | Average_Transaction_Frequency | 0.200665 | 0.359886 | 0.097940 |
| 3 | Card_Type.1 | 0.179430 | 0.321802 | 0.087576 |
| 4 | Card_Type.0 | 0.176801 | 0.317087 | 0.086293 |
| 5 | Channel_ID | 0.162981 | 0.292300 | 0.079547 |
| 6 | Minimum_Transaction_Amount | 0.160161 | 0.287243 | 0.078171 |
| 7 | Transaction_Amount | 0.112477 | 0.201725 | 0.054898 |
| 8 | Maximum_Transaction_Amount | 0.078198 | 0.140246 | 0.038167 |
| 9 | Transaction_Date | 0.077649 | 0.139261 | 0.037899 |
| 10 | Day | 0.068227 | 0.122363 | 0.033300 |
| 11 | City_ID | 0.033867 | 0.060739 | 0.016530 |
| 12 | Month | 0.033847 | 0.060703 | 0.016520 |
| 13 | Average_Transaction_Amount | 0.000852 | 0.001529 | 0.000416 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201634 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.006708 ) | nlambda = 30, lambda.max = 8.5117, lambda.min = 0.006708, lambda.1... | 14 | 14 | 32 | automl_training_py_874_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04843880131415876 RMSE: 0.2200881671379876 LogLoss: 0.1869165221865729 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.9592337938575 Residual deviance: 2910.6640834893137 AIC: 2940.6640834893137 AUC: 0.7742778899003278 AUCPR: 0.27572335314675656 Gini: 0.5485557798006555 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.14737325491442252:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6979.0 | 339.0 | 0.0463 | (339.0/7318.0) |
| 1 | 1 | 263.0 | 205.0 | 0.562 | (263.0/468.0) |
| 2 | Total | 7242.0 | 544.0 | 0.0773 | (602.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.147373 | 0.405138 | 176.0 |
| 1 | max f2 | 0.065683 | 0.441617 | 224.0 |
| 2 | max f0point5 | 0.340454 | 0.413907 | 104.0 |
| 3 | max accuracy | 0.590166 | 0.940149 | 6.0 |
| 4 | max precision | 0.590166 | 0.625000 | 6.0 |
| 5 | max recall | 0.016818 | 1.000000 | 384.0 |
| 6 | max specificity | 0.818992 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.147373 | 0.365220 | 176.0 |
| 8 | max min_per_class_accuracy | 0.042913 | 0.694444 | 283.0 |
| 9 | max mean_per_class_accuracy | 0.062970 | 0.713937 | 228.0 |
| 10 | max tns | 0.818992 | 7317.000000 | 0.0 |
| 11 | max fns | 0.818992 | 468.000000 | 0.0 |
| 12 | max fps | 0.001369 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016818 | 468.000000 | 384.0 |
| 14 | max tnr | 0.818992 | 0.999863 | 0.0 |
| 15 | max fnr | 0.818992 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001369 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016818 | 1.000000 | 384.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.440297 | 7.678501 | 7.678501 | 0.461538 | 0.505389 | 0.461538 | 0.505389 | 0.076923 | 0.076923 | 667.850099 | 667.850099 | 0.071184 |
| 1 | 2 | 0.020036 | 0.399591 | 6.612043 | 7.145272 | 0.397436 | 0.418089 | 0.429487 | 0.461739 | 0.066239 | 0.143162 | 561.204252 | 614.527175 | 0.131001 |
| 2 | 3 | 0.030054 | 0.370294 | 7.891793 | 7.394112 | 0.474359 | 0.384507 | 0.444444 | 0.435995 | 0.079060 | 0.222222 | 689.179268 | 639.411206 | 0.204458 |
| 3 | 4 | 0.040072 | 0.345769 | 7.251918 | 7.358563 | 0.435897 | 0.357819 | 0.442308 | 0.416451 | 0.072650 | 0.294872 | 625.191760 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.324175 | 5.758876 | 7.038626 | 0.346154 | 0.335634 | 0.423077 | 0.400288 | 0.057692 | 0.352564 | 475.887574 | 603.862590 | 0.321818 |
| 5 | 6 | 0.100051 | 0.066614 | 2.950992 | 4.997433 | 0.177378 | 0.155075 | 0.300385 | 0.277839 | 0.147436 | 0.500000 | 195.099202 | 399.743261 | 0.425526 |
| 6 | 7 | 0.150013 | 0.053828 | 0.940896 | 3.646411 | 0.056555 | 0.058906 | 0.219178 | 0.204924 | 0.047009 | 0.547009 | -5.910399 | 264.641143 | 0.422384 |
| 7 | 8 | 0.200103 | 0.048935 | 0.895825 | 2.957882 | 0.053846 | 0.051078 | 0.177792 | 0.166413 | 0.044872 | 0.591880 | -10.417488 | 195.788212 | 0.416833 |
| 8 | 9 | 0.300026 | 0.043667 | 0.812592 | 2.243398 | 0.048843 | 0.046108 | 0.134846 | 0.126346 | 0.081197 | 0.673077 | -18.740799 | 124.339766 | 0.396909 |
| 9 | 10 | 0.400077 | 0.039864 | 0.597983 | 1.831912 | 0.035944 | 0.041719 | 0.110112 | 0.105182 | 0.059829 | 0.732906 | -40.201661 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.036738 | 0.684288 | 1.602564 | 0.041131 | 0.038253 | 0.096327 | 0.091807 | 0.068376 | 0.801282 | -31.571199 | 60.256410 | 0.320550 |
| 11 | 12 | 0.600051 | 0.033813 | 0.576627 | 1.431501 | 0.034660 | 0.035235 | 0.086045 | 0.082374 | 0.057692 | 0.858974 | -42.337316 | 43.150136 | 0.275482 |
| 12 | 13 | 0.699974 | 0.031085 | 0.555984 | 1.306519 | 0.033419 | 0.032440 | 0.078532 | 0.075246 | 0.055556 | 0.914530 | -44.401600 | 30.651925 | 0.228277 |
| 13 | 14 | 0.800026 | 0.027784 | 0.384418 | 1.191201 | 0.023107 | 0.029526 | 0.071601 | 0.069528 | 0.038462 | 0.952991 | -61.558211 | 19.120107 | 0.162748 |
| 14 | 15 | 0.899949 | 0.023601 | 0.299376 | 1.092180 | 0.017995 | 0.025910 | 0.065649 | 0.064685 | 0.029915 | 0.982906 | -70.062400 | 9.218010 | 0.088263 |
| 15 | 16 | 1.000000 | 0.001010 | 0.170852 | 1.000000 | 0.010270 | 0.018936 | 0.060108 | 0.060108 | 0.017094 | 1.000000 | -82.914760 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.046838557350491215 RMSE: 0.21642217388819293 LogLoss: 0.18282039822442492 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311404 Residual deviance: 711.9026306859105 AIC: 741.9026306859105 AUC: 0.7626430339545094 AUCPR: 0.3362519354263168 Gini: 0.5252860679090188 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.25657791222813464:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1747.0 | 83.0 | 0.0454 | (83.0/1830.0) |
| 1 | 1 | 62.0 | 55.0 | 0.5299 | (62.0/117.0) |
| 2 | Total | 1809.0 | 138.0 | 0.0745 | (145.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.256578 | 0.431373 | 115.0 |
| 1 | max f2 | 0.114623 | 0.464000 | 133.0 |
| 2 | max f0point5 | 0.363601 | 0.445293 | 56.0 |
| 3 | max accuracy | 0.460584 | 0.944016 | 16.0 |
| 4 | max precision | 0.839239 | 1.000000 | 0.0 |
| 5 | max recall | 0.020544 | 1.000000 | 363.0 |
| 6 | max specificity | 0.839239 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.256578 | 0.393344 | 115.0 |
| 8 | max min_per_class_accuracy | 0.042602 | 0.692308 | 241.0 |
| 9 | max mean_per_class_accuracy | 0.066788 | 0.724520 | 166.0 |
| 10 | max tns | 0.839239 | 1830.000000 | 0.0 |
| 11 | max fns | 0.839239 | 116.000000 | 0.0 |
| 12 | max fps | 0.001158 | 1830.000000 | 399.0 |
| 13 | max tps | 0.020544 | 117.000000 | 363.0 |
| 14 | max tnr | 0.839239 | 1.000000 | 0.0 |
| 15 | max fnr | 0.839239 | 0.991453 | 0.0 |
| 16 | max fpr | 0.001158 | 1.000000 | 399.0 |
| 17 | max tpr | 0.020544 | 1.000000 | 363.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.20 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.445985 | 10.816667 | 10.816667 | 0.650000 | 0.535343 | 0.650000 | 0.535343 | 0.111111 | 0.111111 | 981.666667 | 981.666667 | 0.107286 |
| 1 | 2 | 0.020031 | 0.401667 | 6.130904 | 8.533859 | 0.368421 | 0.424186 | 0.512821 | 0.481190 | 0.059829 | 0.170940 | 513.090418 | 753.385930 | 0.160558 |
| 2 | 3 | 0.030303 | 0.378426 | 9.152564 | 8.743590 | 0.550000 | 0.391800 | 0.525424 | 0.450888 | 0.094017 | 0.264957 | 815.256410 | 774.358974 | 0.249657 |
| 3 | 4 | 0.040062 | 0.351138 | 5.255061 | 7.893820 | 0.315789 | 0.363171 | 0.474359 | 0.429521 | 0.051282 | 0.316239 | 425.506073 | 689.381986 | 0.293835 |
| 4 | 5 | 0.050334 | 0.324430 | 3.328205 | 6.962062 | 0.200000 | 0.338454 | 0.418367 | 0.410936 | 0.034188 | 0.350427 | 232.820513 | 596.206175 | 0.319280 |
| 5 | 6 | 0.100154 | 0.071698 | 3.431139 | 5.205654 | 0.206186 | 0.192426 | 0.312821 | 0.302241 | 0.170940 | 0.521368 | 243.113931 | 420.565417 | 0.448143 |
| 6 | 7 | 0.149974 | 0.053237 | 0.514671 | 3.647348 | 0.030928 | 0.059823 | 0.219178 | 0.221712 | 0.025641 | 0.547009 | -48.532910 | 264.734809 | 0.422418 |
| 7 | 8 | 0.200308 | 0.048408 | 0.849032 | 2.944181 | 0.051020 | 0.050593 | 0.176923 | 0.178713 | 0.042735 | 0.589744 | -15.096808 | 194.418146 | 0.414334 |
| 8 | 9 | 0.299949 | 0.043260 | 0.772006 | 2.222603 | 0.046392 | 0.045523 | 0.133562 | 0.134468 | 0.076923 | 0.666667 | -22.799366 | 122.260274 | 0.390164 |
| 9 | 10 | 0.400103 | 0.039333 | 0.341354 | 1.751687 | 0.020513 | 0.041184 | 0.105263 | 0.111117 | 0.034188 | 0.700855 | -65.864563 | 75.168691 | 0.319980 |
| 10 | 11 | 0.500257 | 0.035924 | 0.682709 | 1.537672 | 0.041026 | 0.037638 | 0.092402 | 0.096406 | 0.068376 | 0.769231 | -31.729126 | 53.767177 | 0.286171 |
| 11 | 12 | 0.599897 | 0.033181 | 0.772006 | 1.410498 | 0.046392 | 0.034530 | 0.084760 | 0.086129 | 0.076923 | 0.846154 | -22.799366 | 41.049789 | 0.262001 |
| 12 | 13 | 0.700051 | 0.030131 | 0.256016 | 1.245330 | 0.015385 | 0.031620 | 0.074835 | 0.078331 | 0.025641 | 0.871795 | -74.398422 | 24.532987 | 0.182724 |
| 13 | 14 | 0.799692 | 0.027175 | 0.514671 | 1.154291 | 0.030928 | 0.028612 | 0.069364 | 0.072136 | 0.051282 | 0.923077 | -48.532910 | 15.429080 | 0.131274 |
| 14 | 15 | 0.899846 | 0.022826 | 0.512032 | 1.082806 | 0.030769 | 0.025229 | 0.065068 | 0.066915 | 0.051282 | 0.974359 | -48.796844 | 8.280646 | 0.079277 |
| 15 | 16 | 1.000000 | 0.001041 | 0.256016 | 1.000000 | 0.015385 | 0.018100 | 0.060092 | 0.062026 | 0.025641 | 1.000000 | -74.398422 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04874875507490239 RMSE: 0.220791202440003 LogLoss: 0.1883294652491254 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.5116490741552 Residual deviance: 2932.6664328593806 AIC: 2962.6664328593806 AUC: 0.7634871164182451 AUCPR: 0.2642136743257715 Gini: 0.5269742328364901 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.275732803798056:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7037.0 | 281.0 | 0.0384 | (281.0/7318.0) |
| 1 | 1 | 278.0 | 190.0 | 0.594 | (278.0/468.0) |
| 2 | Total | 7315.0 | 471.0 | 0.0718 | (559.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.275733 | 0.404686 | 141.0 |
| 1 | max f2 | 0.066929 | 0.435685 | 222.0 |
| 2 | max f0point5 | 0.317494 | 0.410232 | 119.0 |
| 3 | max accuracy | 0.619445 | 0.939892 | 5.0 |
| 4 | max precision | 0.619445 | 0.500000 | 5.0 |
| 5 | max recall | 0.016052 | 1.000000 | 385.0 |
| 6 | max specificity | 0.819215 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.275733 | 0.366487 | 141.0 |
| 8 | max min_per_class_accuracy | 0.042525 | 0.683761 | 282.0 |
| 9 | max mean_per_class_accuracy | 0.062044 | 0.710887 | 229.0 |
| 10 | max tns | 0.819215 | 7317.000000 | 0.0 |
| 11 | max fns | 0.819215 | 468.000000 | 0.0 |
| 12 | max fps | 0.001278 | 7318.000000 | 399.0 |
| 13 | max tps | 0.016052 | 468.000000 | 385.0 |
| 14 | max tnr | 0.819215 | 0.999863 | 0.0 |
| 15 | max fnr | 0.819215 | 1.000000 | 0.0 |
| 16 | max fpr | 0.001278 | 1.000000 | 399.0 |
| 17 | max tpr | 0.016052 | 1.000000 | 385.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.440751 | 7.251918 | 7.251918 | 0.435897 | 0.509431 | 0.435897 | 0.509431 | 0.072650 | 0.072650 | 625.191760 | 625.191760 | 0.066637 |
| 1 | 2 | 0.020036 | 0.397951 | 7.038626 | 7.145272 | 0.423077 | 0.417329 | 0.429487 | 0.463380 | 0.070513 | 0.143162 | 603.862590 | 614.527175 | 0.131001 |
| 2 | 3 | 0.030054 | 0.368605 | 6.825334 | 7.038626 | 0.410256 | 0.383523 | 0.423077 | 0.436761 | 0.068376 | 0.211538 | 582.533421 | 603.862590 | 0.193091 |
| 3 | 4 | 0.040072 | 0.344451 | 7.038626 | 7.038626 | 0.423077 | 0.356191 | 0.423077 | 0.416619 | 0.070513 | 0.282051 | 603.862590 | 603.862590 | 0.257454 |
| 4 | 5 | 0.050090 | 0.322271 | 6.612043 | 6.953309 | 0.397436 | 0.335341 | 0.417949 | 0.400363 | 0.066239 | 0.348291 | 561.204252 | 595.330923 | 0.317271 |
| 5 | 6 | 0.100051 | 0.066655 | 2.908224 | 4.933363 | 0.174807 | 0.154862 | 0.296534 | 0.277770 | 0.145299 | 0.493590 | 190.822402 | 393.336296 | 0.418706 |
| 6 | 7 | 0.150013 | 0.053678 | 0.769824 | 3.546705 | 0.046272 | 0.058940 | 0.213185 | 0.204889 | 0.038462 | 0.532051 | -23.017599 | 254.670486 | 0.406471 |
| 7 | 8 | 0.200103 | 0.049117 | 1.151775 | 2.947204 | 0.069231 | 0.051086 | 0.177150 | 0.166389 | 0.057692 | 0.589744 | 15.177515 | 194.720384 | 0.414559 |
| 8 | 9 | 0.300026 | 0.043667 | 0.662904 | 2.186422 | 0.039846 | 0.046171 | 0.131421 | 0.126351 | 0.066239 | 0.655983 | -33.709599 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.039837 | 0.640696 | 1.799867 | 0.038511 | 0.041709 | 0.108186 | 0.105183 | 0.064103 | 0.720085 | -35.930351 | 79.986692 | 0.340474 |
| 10 | 11 | 0.500000 | 0.036773 | 0.748440 | 1.589744 | 0.044987 | 0.038261 | 0.095556 | 0.091809 | 0.074786 | 0.794872 | -25.155999 | 58.974359 | 0.313729 |
| 11 | 12 | 0.600051 | 0.033841 | 0.491201 | 1.406575 | 0.029525 | 0.035278 | 0.084546 | 0.082383 | 0.049145 | 0.844017 | -50.879936 | 40.657472 | 0.259568 |
| 12 | 13 | 0.699974 | 0.031052 | 0.513216 | 1.279046 | 0.030848 | 0.032480 | 0.076881 | 0.075259 | 0.051282 | 0.895299 | -48.678400 | 27.904571 | 0.207816 |
| 13 | 14 | 0.800026 | 0.027888 | 0.427131 | 1.172505 | 0.025674 | 0.029534 | 0.070477 | 0.069541 | 0.042735 | 0.938034 | -57.286901 | 17.250509 | 0.146834 |
| 14 | 15 | 0.899949 | 0.023536 | 0.406296 | 1.087431 | 0.024422 | 0.025963 | 0.065363 | 0.064703 | 0.040598 | 0.978632 | -59.370400 | 8.743149 | 0.083716 |
| 15 | 16 | 1.000000 | 0.000920 | 0.213565 | 1.000000 | 0.012837 | 0.018959 | 0.060108 | 0.060126 | 0.021368 | 1.000000 | -78.643450 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.93308187 | 0.015803907 | 0.9346154 | 0.9269231 | 0.9346154 | 0.95 | 0.9307692 | 0.95384616 | 0.91923076 | 0.9269231 | 0.9269231 | 0.9461538 | 0.9269231 | 0.95384616 | 0.9230769 | 0.9307692 | 0.90384614 | 0.9653846 | 0.90733594 | 0.94208497 | 0.93436295 | 0.94208497 | 0.93822396 | 0.96138996 | 0.9227799 | 0.93822396 | 0.9034749 | 0.95366794 | 0.9266409 | 0.9150579 | 0.93050194 | 0.9227799 |
| 1 | auc | 0.7706667 | 0.07261466 | 0.65994626 | 0.758709 | 0.68544215 | 0.79755104 | 0.75504035 | 0.88227963 | 0.8265338 | 0.9082992 | 0.68977886 | 0.8484 | 0.8070685 | 0.6325132 | 0.8400116 | 0.8244977 | 0.7930655 | 0.8356562 | 0.73997235 | 0.78311324 | 0.7188755 | 0.7451754 | 0.7144578 | 0.89504373 | 0.71794873 | 0.73713994 | 0.72119594 | 0.7664609 | 0.795333 | 0.6315789 | 0.82272935 | 0.7861842 |
| 2 | err | 0.06691813 | 0.015803907 | 0.06538462 | 0.073076926 | 0.06538462 | 0.05 | 0.06923077 | 0.046153847 | 0.08076923 | 0.073076926 | 0.073076926 | 0.053846154 | 0.073076926 | 0.046153847 | 0.07692308 | 0.06923077 | 0.09615385 | 0.034615386 | 0.09266409 | 0.057915058 | 0.06563707 | 0.057915058 | 0.06177606 | 0.038610037 | 0.077220075 | 0.06177606 | 0.096525095 | 0.046332046 | 0.07335907 | 0.08494209 | 0.06949807 | 0.077220075 |
| 3 | err_count | 17.366667 | 4.097799 | 17.0 | 19.0 | 17.0 | 13.0 | 18.0 | 12.0 | 21.0 | 19.0 | 19.0 | 14.0 | 19.0 | 12.0 | 20.0 | 18.0 | 25.0 | 9.0 | 24.0 | 15.0 | 17.0 | 15.0 | 16.0 | 10.0 | 20.0 | 16.0 | 25.0 | 12.0 | 19.0 | 22.0 | 18.0 | 20.0 |
| 4 | f0point5 | 0.45802918 | 0.1167217 | 0.3125 | 0.46296296 | 0.36363637 | 0.5633803 | 0.32894737 | 0.56179774 | 0.34653464 | 0.45 | 0.5194805 | 0.42682928 | 0.4494382 | 0.4 | 0.4090909 | 0.45454547 | 0.47244096 | 0.7352941 | 0.3773585 | 0.65217394 | 0.2857143 | 0.60240966 | 0.25862068 | 0.6451613 | 0.36082473 | 0.44642857 | 0.33333334 | 0.625 | 0.47619048 | 0.4368932 | 0.5102041 | 0.47368422 |
| 5 | f1 | 0.45264515 | 0.09875489 | 0.32 | 0.51282054 | 0.32 | 0.55172414 | 0.35714287 | 0.625 | 0.4 | 0.4864865 | 0.45714286 | 0.5 | 0.45714286 | 0.25 | 0.47368422 | 0.4375 | 0.48979592 | 0.5263158 | 0.4 | 0.54545456 | 0.32 | 0.5714286 | 0.27272728 | 0.61538464 | 0.4117647 | 0.3846154 | 0.35897437 | 0.5714286 | 0.51282054 | 0.45 | 0.5263158 | 0.47368422 |
| 6 | f2 | 0.4611446 | 0.11123902 | 0.32786885 | 0.57471263 | 0.2857143 | 0.5405405 | 0.390625 | 0.70422536 | 0.47297296 | 0.5294118 | 0.40816328 | 0.6034483 | 0.4651163 | 0.18181819 | 0.5625 | 0.42168674 | 0.5084746 | 0.40983605 | 0.42553192 | 0.46875 | 0.36363637 | 0.54347825 | 0.28846154 | 0.5882353 | 0.47945204 | 0.33783785 | 0.3888889 | 0.5263158 | 0.5555556 | 0.46391752 | 0.54347825 | 0.47368422 |
| 7 | lift_top_group | 7.598997 | 5.2531667 | 7.2222223 | 5.4166665 | 5.7777777 | 0.0 | 7.2222223 | 13.333333 | 0.0 | 10.833333 | 8.253968 | 17.333334 | 10.196078 | 13.333333 | 0.0 | 10.196078 | 3.768116 | 18.571428 | 9.592592 | 4.111111 | 0.0 | 9.087719 | 8.633333 | 18.5 | 6.6410255 | 5.3958335 | 0.0 | 10.791667 | 5.0784316 | 4.5438595 | 9.592592 | 4.5438595 |
| 8 | logloss | 0.18688467 | 0.032212723 | 0.17133063 | 0.19607213 | 0.2051479 | 0.16663098 | 0.16319424 | 0.12784757 | 0.16181174 | 0.17129506 | 0.2454626 | 0.12446343 | 0.19243516 | 0.1918804 | 0.18434407 | 0.19540314 | 0.24483256 | 0.15161568 | 0.22868112 | 0.22335838 | 0.1497548 | 0.20235452 | 0.15413405 | 0.1437287 | 0.16955933 | 0.21250898 | 0.21557868 | 0.1824639 | 0.19709612 | 0.22858606 | 0.19066453 | 0.21430384 |
| 9 | max_per_class_error | 0.5263757 | 0.13228735 | 0.6666667 | 0.375 | 0.73333335 | 0.46666667 | 0.5833333 | 0.23076923 | 0.46153846 | 0.4375 | 0.61904764 | 0.3 | 0.5294118 | 0.84615386 | 0.35714287 | 0.5882353 | 0.47826087 | 0.64285713 | 0.5555556 | 0.5714286 | 0.6 | 0.47368422 | 0.7 | 0.42857143 | 0.46153846 | 0.6875 | 0.5882353 | 0.5 | 0.4117647 | 0.5263158 | 0.44444445 | 0.5263158 |
| 10 | mcc | 0.42908978 | 0.10190354 | 0.2859668 | 0.48383936 | 0.29362386 | 0.5256315 | 0.32506365 | 0.61364394 | 0.37411523 | 0.45272473 | 0.42957726 | 0.4969697 | 0.41819814 | 0.3057016 | 0.45373735 | 0.40165952 | 0.43793777 | 0.5869734 | 0.35240635 | 0.54015356 | 0.29349014 | 0.54291356 | 0.24183914 | 0.59714633 | 0.38519743 | 0.3647817 | 0.31070134 | 0.5538034 | 0.4784671 | 0.4046938 | 0.48971808 | 0.43201753 |
| 11 | mean_per_class_accuracy | 0.7180197 | 0.06265853 | 0.6485215 | 0.78586066 | 0.62108845 | 0.7544218 | 0.6861559 | 0.8663968 | 0.7388664 | 0.75665987 | 0.6779239 | 0.828 | 0.714718 | 0.5748988 | 0.79094076 | 0.6894215 | 0.7313337 | 0.6785714 | 0.69317657 | 0.7079832 | 0.67791164 | 0.7506579 | 0.6319277 | 0.777551 | 0.74077547 | 0.64596194 | 0.67489064 | 0.74176955 | 0.76932424 | 0.7118421 | 0.7570309 | 0.7160088 |
| 12 | mean_per_class_error | 0.2819803 | 0.06265853 | 0.3514785 | 0.21413934 | 0.37891155 | 0.24557823 | 0.31384408 | 0.13360325 | 0.2611336 | 0.24334016 | 0.3220761 | 0.172 | 0.28528202 | 0.42510122 | 0.20905924 | 0.31057855 | 0.2686663 | 0.32142857 | 0.30682343 | 0.2920168 | 0.32208836 | 0.2493421 | 0.3680723 | 0.22244897 | 0.2592245 | 0.35403806 | 0.3251094 | 0.25823045 | 0.23067574 | 0.28815788 | 0.24296911 | 0.28399122 |
| 13 | mse | 0.048382785 | 0.009549993 | 0.041733243 | 0.051735558 | 0.05163768 | 0.04275165 | 0.04137237 | 0.032533854 | 0.04212611 | 0.0458784 | 0.064623 | 0.030776719 | 0.051227733 | 0.046361748 | 0.048422325 | 0.051976122 | 0.06794553 | 0.038443446 | 0.060661267 | 0.059813213 | 0.036186483 | 0.05268343 | 0.03597809 | 0.0366035 | 0.042577017 | 0.05462002 | 0.056470964 | 0.04597166 | 0.052170794 | 0.06002919 | 0.050855447 | 0.057316996 |
| 14 | null_deviance | 118.01705 | 18.356863 | 98.28862 | 120.223526 | 114.7255 | 114.7255 | 98.28862 | 103.75808 | 103.75808 | 120.223526 | 147.85797 | 87.37807 | 125.73113 | 103.75808 | 109.23703 | 125.73113 | 158.97968 | 109.23703 | 131.12575 | 147.73709 | 87.25086 | 136.65318 | 87.25086 | 109.11213 | 103.63261 | 120.09978 | 125.607956 | 120.09978 | 125.607956 | 136.65318 | 131.12575 | 136.65318 |
| 15 | pr_auc | 0.30577648 | 0.1217217 | 0.1358033 | 0.2649433 | 0.21824445 | 0.31158295 | 0.16353454 | 0.5505439 | 0.23819648 | 0.43369076 | 0.30640018 | 0.3680456 | 0.32371485 | 0.18009938 | 0.21105115 | 0.33443704 | 0.30705848 | 0.54364294 | 0.24872783 | 0.38100052 | 0.13670641 | 0.43495813 | 0.19314244 | 0.5706578 | 0.21792483 | 0.20167911 | 0.16585441 | 0.42350844 | 0.30621216 | 0.25085282 | 0.4086557 | 0.34242427 |
| 16 | precision | 0.47570196 | 0.16263753 | 0.30769232 | 0.4347826 | 0.4 | 0.5714286 | 0.3125 | 0.5263158 | 0.3181818 | 0.42857143 | 0.5714286 | 0.3888889 | 0.44444445 | 0.6666667 | 0.375 | 0.46666667 | 0.46153846 | 1.0 | 0.36363637 | 0.75 | 0.26666668 | 0.625 | 0.25 | 0.6666667 | 0.33333334 | 0.5 | 0.3181818 | 0.6666667 | 0.45454547 | 0.42857143 | 0.5 | 0.47368422 |
| 17 | r2 | 0.13700902 | 0.07957734 | 0.052027173 | 0.10416915 | 0.05014772 | 0.21360229 | 0.060224403 | 0.31507677 | 0.11313448 | 0.2055892 | 0.12960455 | 0.16779752 | 0.16170546 | 0.023963222 | 0.049550153 | 0.1494588 | 0.15738069 | 0.24541903 | 0.06195976 | 0.19721285 | 0.025130294 | 0.22498745 | 0.030744454 | 0.2841401 | 0.1069078 | 0.057622008 | 0.079210326 | 0.20683509 | 0.14932692 | 0.11692587 | 0.21359286 | 0.15682428 |
| 18 | recall | 0.4736243 | 0.13228735 | 0.33333334 | 0.625 | 0.26666668 | 0.53333336 | 0.41666666 | 0.7692308 | 0.53846157 | 0.5625 | 0.3809524 | 0.7 | 0.47058824 | 0.15384616 | 0.64285713 | 0.4117647 | 0.5217391 | 0.35714287 | 0.44444445 | 0.42857143 | 0.4 | 0.5263158 | 0.3 | 0.5714286 | 0.53846157 | 0.3125 | 0.4117647 | 0.5 | 0.5882353 | 0.47368422 | 0.5555556 | 0.47368422 |
| 19 | residual_deviance | 96.99918 | 16.687357 | 89.09193 | 101.95751 | 106.67691 | 86.64811 | 84.861 | 66.480736 | 84.142105 | 89.07343 | 127.64055 | 64.720985 | 100.066284 | 99.77782 | 95.85891 | 101.609634 | 127.312935 | 78.84016 | 118.45682 | 115.69964 | 77.57299 | 104.81964 | 79.84144 | 74.45147 | 87.83173 | 110.07965 | 111.669754 | 94.5163 | 102.095795 | 118.407585 | 98.76423 | 111.009384 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:16:42 | 0.000 sec | 2 | .85E1 | 15 | 0.452180 | 0.451801 | 0.452507 | 0.012858 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:16:42 | 0.005 sec | 4 | .53E1 | 15 | 0.450781 | 0.450226 | 0.451168 | 0.012808 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:16:42 | 0.012 sec | 6 | .33E1 | 15 | 0.448582 | 0.447750 | 0.449062 | 0.012730 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:16:42 | 0.018 sec | 8 | .2E1 | 15 | 0.445163 | 0.443900 | 0.445783 | 0.012610 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:16:42 | 0.024 sec | 10 | .13E1 | 15 | 0.440006 | 0.438093 | 0.440827 | 0.012433 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:16:42 | 0.029 sec | 12 | .79E0 | 15 | 0.432571 | 0.429721 | 0.433652 | 0.012188 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:16:42 | 0.043 sec | 14 | .49E0 | 15 | 0.422650 | 0.418554 | 0.424017 | 0.011881 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:16:42 | 0.055 sec | 16 | .3E0 | 15 | 0.411014 | 0.405471 | 0.412605 | 0.011562 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:16:42 | 0.060 sec | 18 | .19E0 | 15 | 0.399553 | 0.392629 | 0.401245 | 0.011313 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:16:42 | 0.064 sec | 20 | .12E0 | 15 | 0.390205 | 0.382242 | 0.391931 | 0.011198 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:16:42 | 0.067 sec | 22 | .73E-1 | 15 | 0.383645 | 0.375081 | 0.385434 | 0.011211 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:16:42 | 0.071 sec | 24 | .45E-1 | 15 | 0.379455 | 0.370665 | 0.381384 | 0.011306 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:16:42 | 0.074 sec | 26 | .28E-1 | 15 | 0.376892 | 0.368126 | 0.379027 | 0.011438 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:16:42 | 0.078 sec | 28 | .17E-1 | 15 | 0.375335 | 0.366731 | 0.377713 | 0.011576 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:16:42 | 0.082 sec | 30 | .11E-1 | 15 | 0.374393 | 0.365999 | 0.377018 | 0.011704 | 0.0 | 30.0 | 0.220088 | 0.186917 | 0.142599 | 0.774278 | 0.275723 | 7.678501 | 0.077318 | 0.216422 | 0.18282 | 0.170725 | 0.762643 | 0.336252 | 10.816667 | 0.074474 | |
| 15 | 2021-07-15 20:16:42 | 0.085 sec | 32 | .67E-2 | 15 | 0.373833 | 0.365641 | 0.376686 | 0.011812 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:16:42 | 0.090 sec | 34 | .42E-2 | 15 | 0.373512 | 0.365492 | 0.379206 | 0.012097 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-15 20:16:42 | 0.094 sec | 36 | .26E-2 | 15 | 0.373340 | 0.365448 | 0.384039 | 0.013479 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:16:42 | 0.096 sec | 37 | .16E-2 | 15 | 0.373255 | 0.365449 | 0.387502 | 0.014205 | 0.0 | NaN |
Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.565465 | 1.000000 | 0.256170 |
| 1 | Average_Transaction_Frequency | 0.259314 | 0.458586 | 0.117476 |
| 2 | Merchant_ID | 0.214539 | 0.379402 | 0.097192 |
| 3 | Card_Type.1 | 0.177167 | 0.313313 | 0.080261 |
| 4 | Card_Type.0 | 0.174599 | 0.308771 | 0.079098 |
| 5 | Minimum_Transaction_Amount | 0.163385 | 0.288939 | 0.074018 |
| 6 | Channel_ID | 0.162379 | 0.287161 | 0.073562 |
| 7 | Transaction_Amount | 0.124265 | 0.219757 | 0.056295 |
| 8 | Transaction_Date | 0.088217 | 0.156009 | 0.039965 |
| 9 | Maximum_Transaction_Amount | 0.082522 | 0.145937 | 0.037385 |
| 10 | Month | 0.067987 | 0.120231 | 0.030800 |
| 11 | Day | 0.049375 | 0.087317 | 0.022368 |
| 12 | Average_Transaction_Amount | 0.039245 | 0.069404 | 0.017779 |
| 13 | City_ID | 0.038919 | 0.068826 | 0.017631 |
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201647 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.004202 ) | nlambda = 30, lambda.max = 8.5862, lambda.min = 0.004202, lambda.1... | 14 | 14 | 34 | automl_training_py_909_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04813232537278134 RMSE: 0.2193908051235998 LogLoss: 0.18637933900220385 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793846 Residual deviance: 2902.299066942318 AIC: 2932.299066942318 AUC: 0.7707934772706568 AUCPR: 0.289212154938732 Gini: 0.5415869545413137 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.14313113283612258:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6967.0 | 351.0 | 0.048 | (351.0/7318.0) |
| 1 | 1 | 258.0 | 210.0 | 0.5513 | (258.0/468.0) |
| 2 | Total | 7225.0 | 561.0 | 0.0782 | (609.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.143131 | 0.408163 | 185.0 |
| 1 | max f2 | 0.060570 | 0.439140 | 232.0 |
| 2 | max f0point5 | 0.354100 | 0.421651 | 100.0 |
| 3 | max accuracy | 0.558818 | 0.940791 | 15.0 |
| 4 | max precision | 0.862623 | 1.000000 | 0.0 |
| 5 | max recall | 0.017653 | 1.000000 | 381.0 |
| 6 | max specificity | 0.862623 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.281847 | 0.368653 | 148.0 |
| 8 | max min_per_class_accuracy | 0.040479 | 0.686526 | 288.0 |
| 9 | max mean_per_class_accuracy | 0.060570 | 0.714366 | 232.0 |
| 10 | max tns | 0.862623 | 7318.000000 | 0.0 |
| 11 | max fns | 0.862623 | 467.000000 | 0.0 |
| 12 | max fps | 0.000954 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017653 | 468.000000 | 381.0 |
| 14 | max tnr | 0.862623 | 1.000000 | 0.0 |
| 15 | max fnr | 0.862623 | 0.997863 | 0.0 |
| 16 | max fpr | 0.000954 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017653 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.432753 | 8.318376 | 8.318376 | 0.500000 | 0.523657 | 0.500000 | 0.523657 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.395496 | 8.105084 | 8.211730 | 0.487179 | 0.413156 | 0.493590 | 0.468407 | 0.081197 | 0.164530 | 710.508437 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.371288 | 6.825334 | 7.749598 | 0.410256 | 0.382717 | 0.465812 | 0.439843 | 0.068376 | 0.232906 | 582.533421 | 674.959822 | 0.215825 |
| 3 | 4 | 0.040072 | 0.351103 | 7.038626 | 7.571855 | 0.423077 | 0.361164 | 0.455128 | 0.420174 | 0.070513 | 0.303419 | 603.862590 | 657.185514 | 0.280188 |
| 4 | 5 | 0.050090 | 0.329267 | 4.692417 | 6.995968 | 0.282051 | 0.341378 | 0.420513 | 0.404415 | 0.047009 | 0.350427 | 369.241727 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.065548 | 2.822688 | 4.912006 | 0.169666 | 0.168446 | 0.295250 | 0.286582 | 0.141026 | 0.491453 | 182.268802 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.051579 | 1.154736 | 3.660655 | 0.069409 | 0.056876 | 0.220034 | 0.210079 | 0.057692 | 0.549145 | 15.473601 | 266.065522 | 0.424658 |
| 7 | 8 | 0.200103 | 0.046690 | 0.853167 | 2.957882 | 0.051282 | 0.048764 | 0.177792 | 0.169698 | 0.042735 | 0.591880 | -14.683322 | 195.788212 | 0.416833 |
| 8 | 9 | 0.300026 | 0.041670 | 0.748440 | 2.222032 | 0.044987 | 0.043930 | 0.133562 | 0.127811 | 0.074786 | 0.666667 | -25.155999 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.038420 | 0.662053 | 1.831912 | 0.039795 | 0.039922 | 0.110112 | 0.105832 | 0.066239 | 0.732906 | -33.794696 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.035857 | 0.620136 | 1.589744 | 0.037275 | 0.037116 | 0.095556 | 0.092099 | 0.061966 | 0.794872 | -37.986399 | 58.974359 | 0.313729 |
| 11 | 12 | 0.600051 | 0.033391 | 0.619340 | 1.427940 | 0.037227 | 0.034596 | 0.085830 | 0.082511 | 0.061966 | 0.856838 | -38.066006 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.030890 | 0.513216 | 1.297361 | 0.030848 | 0.032115 | 0.077982 | 0.075317 | 0.051282 | 0.908120 | -48.678400 | 29.736141 | 0.221457 |
| 13 | 14 | 0.800026 | 0.027827 | 0.384418 | 1.183189 | 0.023107 | 0.029418 | 0.071119 | 0.069577 | 0.038462 | 0.946581 | -61.558211 | 18.318850 | 0.155928 |
| 14 | 15 | 0.899949 | 0.023557 | 0.213840 | 1.075560 | 0.012853 | 0.025859 | 0.064650 | 0.064723 | 0.021368 | 0.967949 | -78.616000 | 7.555997 | 0.072349 |
| 15 | 16 | 1.000000 | 0.000731 | 0.320348 | 1.000000 | 0.019255 | 0.018596 | 0.060108 | 0.060108 | 0.032051 | 1.000000 | -67.965176 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04782450600267793 RMSE: 0.21868814783311402 LogLoss: 0.18447730336658244 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311447 Residual deviance: 718.3546193094721 AIC: 748.3546193094721 AUC: 0.7905539208817898 AUCPR: 0.29432069272826045 Gini: 0.5811078417635795 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.21146723168373893:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1752.0 | 78.0 | 0.0426 | (78.0/1830.0) |
| 1 | 1 | 66.0 | 51.0 | 0.5641 | (66.0/117.0) |
| 2 | Total | 1818.0 | 129.0 | 0.074 | (144.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.211467 | 0.414634 | 111.0 |
| 1 | max f2 | 0.076076 | 0.452418 | 149.0 |
| 2 | max f0point5 | 0.338572 | 0.442890 | 64.0 |
| 3 | max accuracy | 0.441536 | 0.942476 | 15.0 |
| 4 | max precision | 0.471693 | 0.666667 | 11.0 |
| 5 | max recall | 0.019966 | 1.000000 | 369.0 |
| 6 | max specificity | 0.582110 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.211467 | 0.375770 | 111.0 |
| 8 | max min_per_class_accuracy | 0.041158 | 0.716393 | 242.0 |
| 9 | max mean_per_class_accuracy | 0.040708 | 0.718166 | 244.0 |
| 10 | max tns | 0.582110 | 1829.000000 | 0.0 |
| 11 | max fns | 0.582110 | 117.000000 | 0.0 |
| 12 | max fps | 0.000962 | 1830.000000 | 399.0 |
| 13 | max tps | 0.019966 | 117.000000 | 369.0 |
| 14 | max tnr | 0.582110 | 0.999454 | 0.0 |
| 15 | max fnr | 0.582110 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000962 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019966 | 1.000000 | 369.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.85 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.425889 | 9.984615 | 9.984615 | 0.600000 | 0.481839 | 0.600000 | 0.481839 | 0.102564 | 0.102564 | 898.461538 | 898.461538 | 0.098193 |
| 1 | 2 | 0.020031 | 0.397049 | 6.130904 | 8.107166 | 0.368421 | 0.410298 | 0.487179 | 0.446986 | 0.059829 | 0.162393 | 513.090418 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.365072 | 7.488462 | 7.897436 | 0.450000 | 0.382049 | 0.474576 | 0.424973 | 0.076923 | 0.239316 | 648.846154 | 689.743590 | 0.222376 |
| 3 | 4 | 0.040062 | 0.337203 | 8.758435 | 8.107166 | 0.526316 | 0.351274 | 0.487179 | 0.407021 | 0.085470 | 0.324786 | 775.843455 | 710.716634 | 0.302928 |
| 4 | 5 | 0.050334 | 0.306861 | 4.160256 | 7.301675 | 0.250000 | 0.321644 | 0.438776 | 0.389597 | 0.042735 | 0.367521 | 316.025641 | 630.167452 | 0.337467 |
| 5 | 6 | 0.100154 | 0.066541 | 2.744911 | 5.034977 | 0.164948 | 0.157343 | 0.302564 | 0.274065 | 0.136752 | 0.504274 | 174.491145 | 403.497699 | 0.429957 |
| 6 | 7 | 0.149974 | 0.051031 | 0.343114 | 3.476379 | 0.020619 | 0.057350 | 0.208904 | 0.202074 | 0.017094 | 0.521368 | -65.688607 | 247.637864 | 0.395138 |
| 7 | 8 | 0.200308 | 0.046055 | 1.188645 | 2.901512 | 0.071429 | 0.048291 | 0.174359 | 0.163431 | 0.059829 | 0.581197 | 18.864469 | 190.151216 | 0.405240 |
| 8 | 9 | 0.299949 | 0.041386 | 1.200899 | 2.336582 | 0.072165 | 0.043356 | 0.140411 | 0.123543 | 0.119658 | 0.700855 | 20.089876 | 133.658237 | 0.426538 |
| 9 | 10 | 0.400103 | 0.037682 | 0.597370 | 1.901221 | 0.035897 | 0.039313 | 0.114249 | 0.102459 | 0.059829 | 0.760684 | -40.262985 | 90.122116 | 0.383635 |
| 10 | 11 | 0.500257 | 0.035470 | 0.938725 | 1.708524 | 0.056410 | 0.036502 | 0.102669 | 0.089254 | 0.094017 | 0.854701 | -6.127548 | 70.852419 | 0.377105 |
| 11 | 12 | 0.599897 | 0.033071 | 0.343114 | 1.481735 | 0.020619 | 0.034212 | 0.089041 | 0.080112 | 0.034188 | 0.888889 | -65.688607 | 48.173516 | 0.307468 |
| 12 | 13 | 0.700051 | 0.030565 | 0.341354 | 1.318585 | 0.020513 | 0.031775 | 0.079237 | 0.073196 | 0.034188 | 0.923077 | -65.864563 | 31.858457 | 0.237285 |
| 13 | 14 | 0.799692 | 0.027585 | 0.428892 | 1.207730 | 0.025773 | 0.029153 | 0.072575 | 0.067709 | 0.042735 | 0.965812 | -57.110759 | 20.773018 | 0.176741 |
| 14 | 15 | 0.899846 | 0.023553 | 0.170677 | 1.092305 | 0.010256 | 0.025682 | 0.065639 | 0.063031 | 0.017094 | 0.982906 | -82.932281 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.000808 | 0.170677 | 1.000000 | 0.010256 | 0.018189 | 0.060092 | 0.058540 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048314917844115436 RMSE: 0.21980654640868966 LogLoss: 0.1874930722653597 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.536621270318 Residual deviance: 2919.642121316181 AIC: 2949.642121316181 AUC: 0.7614112433222846 AUCPR: 0.2789306901875544 Gini: 0.5228224866445692 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2500729007254136:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7017.0 | 301.0 | 0.0411 | (301.0/7318.0) |
| 1 | 1 | 270.0 | 198.0 | 0.5769 | (270.0/468.0) |
| 2 | Total | 7287.0 | 499.0 | 0.0733 | (571.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.250073 | 0.409514 | 157.0 |
| 1 | max f2 | 0.060166 | 0.433100 | 234.0 |
| 2 | max f0point5 | 0.352674 | 0.408477 | 99.0 |
| 3 | max accuracy | 0.572231 | 0.940663 | 15.0 |
| 4 | max precision | 0.654390 | 0.714286 | 6.0 |
| 5 | max recall | 0.017309 | 1.000000 | 382.0 |
| 6 | max specificity | 0.877201 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.250073 | 0.370677 | 157.0 |
| 8 | max min_per_class_accuracy | 0.041276 | 0.685297 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.059909 | 0.712950 | 235.0 |
| 10 | max tns | 0.877201 | 7317.000000 | 0.0 |
| 11 | max fns | 0.877201 | 468.000000 | 0.0 |
| 12 | max fps | 0.000998 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017309 | 468.000000 | 382.0 |
| 14 | max tnr | 0.877201 | 0.999863 | 0.0 |
| 15 | max fnr | 0.877201 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000998 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017309 | 1.000000 | 382.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.99 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.431033 | 7.891793 | 7.891793 | 0.474359 | 0.519818 | 0.474359 | 0.519818 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.392287 | 8.105084 | 7.998439 | 0.487179 | 0.408719 | 0.480769 | 0.464269 | 0.081197 | 0.160256 | 710.508437 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.369043 | 6.612043 | 7.536307 | 0.397436 | 0.379414 | 0.452991 | 0.435984 | 0.066239 | 0.226496 | 561.204252 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.347586 | 6.825334 | 7.358563 | 0.410256 | 0.357128 | 0.442308 | 0.416270 | 0.068376 | 0.294872 | 582.533421 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.323940 | 4.692417 | 6.825334 | 0.282051 | 0.337127 | 0.410256 | 0.400441 | 0.047009 | 0.341880 | 369.241727 | 582.533421 | 0.310451 |
| 5 | 6 | 0.100051 | 0.063036 | 2.993760 | 4.912006 | 0.179949 | 0.153041 | 0.295250 | 0.276900 | 0.149573 | 0.491453 | 199.376002 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.056793 | 1.154736 | 3.660655 | 0.069409 | 0.059871 | 0.220034 | 0.204619 | 0.057692 | 0.549145 | 15.473601 | 266.065522 | 0.424658 |
| 7 | 8 | 0.200103 | 0.048869 | 0.682533 | 2.915169 | 0.041026 | 0.052080 | 0.175225 | 0.166435 | 0.034188 | 0.583333 | -31.746658 | 191.516902 | 0.407739 |
| 8 | 9 | 0.300026 | 0.042665 | 0.727056 | 2.186422 | 0.043702 | 0.045309 | 0.131421 | 0.126094 | 0.072650 | 0.655983 | -27.294399 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.039113 | 0.768836 | 1.831912 | 0.046213 | 0.040771 | 0.110112 | 0.104757 | 0.076923 | 0.732906 | -23.116421 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.036372 | 0.427680 | 1.551282 | 0.025707 | 0.037736 | 0.093244 | 0.091363 | 0.042735 | 0.775641 | -57.232000 | 55.128205 | 0.293269 |
| 11 | 12 | 0.600051 | 0.033734 | 0.640696 | 1.399453 | 0.038511 | 0.035049 | 0.084118 | 0.081973 | 0.064103 | 0.839744 | -35.930351 | 39.945282 | 0.255021 |
| 12 | 13 | 0.699974 | 0.031193 | 0.598752 | 1.285151 | 0.035990 | 0.032454 | 0.077248 | 0.074904 | 0.059829 | 0.899573 | -40.124800 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.028184 | 0.320348 | 1.164493 | 0.019255 | 0.029771 | 0.069995 | 0.069260 | 0.032051 | 0.931624 | -67.965176 | 16.449252 | 0.140014 |
| 14 | 15 | 0.899949 | 0.023828 | 0.342144 | 1.073186 | 0.020566 | 0.026185 | 0.064507 | 0.064477 | 0.034188 | 0.965812 | -65.785600 | 7.318567 | 0.070075 |
| 15 | 16 | 1.000000 | 0.000777 | 0.341705 | 1.000000 | 0.020539 | 0.018901 | 0.060108 | 0.059917 | 0.034188 | 1.000000 | -65.829521 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9319325 | 0.025578208 | 0.9115385 | 0.9 | 0.9576923 | 0.9461538 | 0.9269231 | 0.9 | 0.93846154 | 0.9076923 | 0.9269231 | 0.9346154 | 0.9307692 | 0.91923076 | 0.9346154 | 0.9576923 | 0.9307692 | 0.96153843 | 0.94208497 | 0.93050194 | 0.95366794 | 0.95366794 | 0.8957529 | 0.9266409 | 0.93436295 | 0.969112 | 0.84555984 | 0.9266409 | 0.95752895 | 0.96525097 | 0.9459459 | 0.9266409 |
| 1 | auc | 0.762986 | 0.07631634 | 0.72020835 | 0.7259227 | 0.7214312 | 0.7930283 | 0.9142023 | 0.7395896 | 0.7705156 | 0.7473531 | 0.83878 | 0.8833212 | 0.8249328 | 0.82575756 | 0.7522957 | 0.6680108 | 0.79253113 | 0.84581786 | 0.80935913 | 0.7336066 | 0.6644315 | 0.8130144 | 0.7408848 | 0.67721194 | 0.764177 | 0.8004016 | 0.6120564 | 0.8002092 | 0.87188756 | 0.6457161 | 0.62103826 | 0.77188754 |
| 2 | err | 0.06806752 | 0.025578208 | 0.08846154 | 0.1 | 0.042307694 | 0.053846154 | 0.073076926 | 0.1 | 0.06153846 | 0.092307694 | 0.073076926 | 0.06538462 | 0.06923077 | 0.08076923 | 0.06538462 | 0.042307694 | 0.06923077 | 0.03846154 | 0.057915058 | 0.06949807 | 0.046332046 | 0.046332046 | 0.1042471 | 0.07335907 | 0.06563707 | 0.03088803 | 0.15444015 | 0.07335907 | 0.042471044 | 0.034749035 | 0.054054055 | 0.07335907 |
| 3 | err_count | 17.666666 | 6.634982 | 23.0 | 26.0 | 11.0 | 14.0 | 19.0 | 26.0 | 16.0 | 24.0 | 19.0 | 17.0 | 18.0 | 21.0 | 17.0 | 11.0 | 18.0 | 10.0 | 15.0 | 18.0 | 12.0 | 12.0 | 27.0 | 19.0 | 17.0 | 8.0 | 40.0 | 19.0 | 11.0 | 9.0 | 14.0 | 19.0 |
| 4 | f0point5 | 0.46578673 | 0.12688833 | 0.39772728 | 0.37815127 | 0.42857143 | 0.57377046 | 0.39130434 | 0.3809524 | 0.5294118 | 0.21052632 | 0.46391752 | 0.53097343 | 0.38043478 | 0.44117647 | 0.51282054 | 0.46875 | 0.5263158 | 0.7746479 | 0.5714286 | 0.42168674 | 0.5263158 | 0.625 | 0.3305785 | 0.41666666 | 0.5 | 0.5952381 | 0.1910828 | 0.5 | 0.48387095 | 0.6756757 | 0.49019608 | 0.25641027 |
| 5 | f1 | 0.44847605 | 0.098334536 | 0.3783784 | 0.4090909 | 0.3529412 | 0.5 | 0.4864865 | 0.3809524 | 0.5294118 | 0.25 | 0.4864865 | 0.58536583 | 0.4375 | 0.46153846 | 0.4848485 | 0.3529412 | 0.5263158 | 0.6875 | 0.516129 | 0.4375 | 0.4 | 0.53846157 | 0.37209302 | 0.42424244 | 0.4516129 | 0.5555556 | 0.23076923 | 0.45714286 | 0.5217391 | 0.5263158 | 0.41666666 | 0.2962963 |
| 6 | f2 | 0.4464339 | 0.100452535 | 0.36082473 | 0.44554454 | 0.3 | 0.443038 | 0.64285713 | 0.3809524 | 0.5294118 | 0.30769232 | 0.5113636 | 0.65217394 | 0.5147059 | 0.48387095 | 0.4597701 | 0.28301886 | 0.5263158 | 0.6179775 | 0.47058824 | 0.45454547 | 0.32258064 | 0.47297296 | 0.42553192 | 0.43209878 | 0.4117647 | 0.5208333 | 0.29126215 | 0.42105263 | 0.5660377 | 0.43103448 | 0.36231884 | 0.3508772 |
| 7 | lift_top_group | 8.203134 | 4.896419 | 4.3333335 | 9.122807 | 7.878788 | 10.196078 | 7.878788 | 0.0 | 5.098039 | 0.0 | 5.098039 | 5.098039 | 0.0 | 9.62963 | 9.62963 | 14.444445 | 4.5614033 | 13.684211 | 9.592592 | 11.511111 | 6.1666665 | 16.1875 | 10.156863 | 5.3958335 | 9.592592 | 17.266666 | 0.0 | 8.633333 | 17.266666 | 13.282051 | 5.7555556 | 8.633333 |
| 8 | logloss | 0.18636669 | 0.03260542 | 0.24428856 | 0.22533733 | 0.15564056 | 0.18899742 | 0.12618236 | 0.2517394 | 0.18479693 | 0.16542803 | 0.18868947 | 0.16908133 | 0.15116997 | 0.19655243 | 0.21029659 | 0.17213129 | 0.19899835 | 0.1750521 | 0.19914357 | 0.17988367 | 0.18863021 | 0.17850283 | 0.21176417 | 0.19749716 | 0.20490494 | 0.13401856 | 0.23588969 | 0.22205867 | 0.11953792 | 0.16569065 | 0.19616818 | 0.1529283 |
| 9 | max_per_class_error | 0.54597044 | 0.123270094 | 0.65 | 0.5263158 | 0.72727275 | 0.5882353 | 0.18181819 | 0.61904764 | 0.47058824 | 0.6363636 | 0.47058824 | 0.29411766 | 0.41666666 | 0.5 | 0.5555556 | 0.75 | 0.47368422 | 0.42105263 | 0.5555556 | 0.53333336 | 0.71428573 | 0.5625 | 0.5294118 | 0.5625 | 0.6111111 | 0.5 | 0.64705884 | 0.6 | 0.4 | 0.61538464 | 0.6666667 | 0.6 |
| 10 | mcc | 0.4252677 | 0.10715261 | 0.33236364 | 0.35957506 | 0.34946984 | 0.4854702 | 0.5031643 | 0.32655907 | 0.49648994 | 0.21819456 | 0.44914004 | 0.56066114 | 0.4180421 | 0.41961062 | 0.45238268 | 0.36962467 | 0.48897138 | 0.6814499 | 0.49347085 | 0.4015085 | 0.41720933 | 0.5312636 | 0.32650575 | 0.3853074 | 0.4239339 | 0.5433689 | 0.16886064 | 0.42365196 | 0.5046277 | 0.5523514 | 0.40422782 | 0.270574 |
| 11 | mean_per_class_accuracy | 0.70827585 | 0.05993818 | 0.65416664 | 0.7036471 | 0.63033956 | 0.6976519 | 0.87495434 | 0.66327953 | 0.748245 | 0.64768165 | 0.74207217 | 0.8282498 | 0.765457 | 0.7252066 | 0.70775944 | 0.62096775 | 0.7444857 | 0.78532434 | 0.7118488 | 0.7128415 | 0.6387755 | 0.71257716 | 0.698104 | 0.6981739 | 0.6819963 | 0.7439759 | 0.61655325 | 0.68535566 | 0.78594375 | 0.6902752 | 0.65847 | 0.6738956 |
| 12 | mean_per_class_error | 0.29172418 | 0.05993818 | 0.34583333 | 0.29635292 | 0.36966047 | 0.3023481 | 0.12504564 | 0.33672047 | 0.25175503 | 0.35231838 | 0.25792786 | 0.17175019 | 0.23454301 | 0.2747934 | 0.2922406 | 0.37903225 | 0.2555143 | 0.2146757 | 0.28815123 | 0.28715846 | 0.3612245 | 0.28742284 | 0.30189598 | 0.30182612 | 0.31800368 | 0.2560241 | 0.38344675 | 0.31464434 | 0.21405622 | 0.30972484 | 0.34153005 | 0.32610443 |
| 13 | mse | 0.048063576 | 0.00965118 | 0.064496316 | 0.060373373 | 0.037184797 | 0.049575035 | 0.03253757 | 0.067704424 | 0.04758204 | 0.041225802 | 0.05088426 | 0.045837972 | 0.038932223 | 0.05288938 | 0.055764697 | 0.041286528 | 0.052850936 | 0.04594302 | 0.052345622 | 0.045467015 | 0.046493143 | 0.045737382 | 0.055761192 | 0.051214315 | 0.053853434 | 0.032400273 | 0.060440842 | 0.059060734 | 0.028494636 | 0.040120754 | 0.04884066 | 0.036608886 |
| 14 | null_deviance | 118.01789 | 18.576435 | 142.31174 | 136.7752 | 92.82863 | 125.73113 | 92.82863 | 147.85797 | 125.73113 | 92.82863 | 125.73113 | 125.73113 | 98.28862 | 131.24835 | 131.24835 | 98.28862 | 136.7752 | 136.7752 | 131.12575 | 114.60118 | 109.11213 | 120.09978 | 125.607956 | 120.09978 | 131.12575 | 87.25086 | 125.607956 | 142.19029 | 87.25086 | 103.63261 | 114.60118 | 87.25086 |
| 15 | pr_auc | 0.3057826 | 0.11474565 | 0.24940026 | 0.2337053 | 0.25733662 | 0.41292053 | 0.2826141 | 0.21975118 | 0.37333107 | 0.11041282 | 0.32695615 | 0.43757194 | 0.21647492 | 0.34615543 | 0.28245395 | 0.25142494 | 0.37477806 | 0.619697 | 0.43352246 | 0.3151317 | 0.26833844 | 0.49218583 | 0.23990041 | 0.2044688 | 0.32989296 | 0.32958278 | 0.11063523 | 0.37011787 | 0.44366908 | 0.32283646 | 0.18974203 | 0.1284695 |
| 16 | precision | 0.48855716 | 0.16440663 | 0.4117647 | 0.36 | 0.5 | 0.6363636 | 0.34615386 | 0.3809524 | 0.5294118 | 0.1904762 | 0.45 | 0.5 | 0.35 | 0.42857143 | 0.53333336 | 0.6 | 0.5263158 | 0.84615386 | 0.61538464 | 0.4117647 | 0.6666667 | 0.7 | 0.30769232 | 0.4117647 | 0.53846157 | 0.625 | 0.17142858 | 0.53333336 | 0.46153846 | 0.8333333 | 0.5555556 | 0.23529412 |
| 17 | r2 | 0.14220265 | 0.07521103 | 0.09167686 | 0.10870496 | 0.08225912 | 0.18875031 | 0.19695519 | 0.08810144 | 0.22136384 | -0.017475054 | 0.16732608 | 0.24990389 | 0.11565244 | 0.17921896 | 0.13459744 | 0.062174257 | 0.21975905 | 0.32174096 | 0.19054942 | 0.16667412 | 0.09072698 | 0.21087722 | 0.090783514 | 0.11638183 | 0.16723323 | 0.12713146 | 0.014479329 | 0.17116046 | 0.23234995 | 0.15843014 | 0.10484143 | 0.013750723 |
| 18 | recall | 0.45402953 | 0.123270094 | 0.35 | 0.47368422 | 0.27272728 | 0.4117647 | 0.8181818 | 0.3809524 | 0.5294118 | 0.36363637 | 0.5294118 | 0.7058824 | 0.5833333 | 0.5 | 0.44444445 | 0.25 | 0.5263158 | 0.57894737 | 0.44444445 | 0.46666667 | 0.2857143 | 0.4375 | 0.47058824 | 0.4375 | 0.3888889 | 0.5 | 0.3529412 | 0.4 | 0.6 | 0.3846154 | 0.33333334 | 0.4 |
| 19 | residual_deviance | 96.738235 | 16.93525 | 127.03005 | 117.17541 | 80.93309 | 98.278656 | 65.61483 | 130.9045 | 96.094406 | 86.022575 | 98.11853 | 87.922295 | 78.60838 | 102.20726 | 109.354225 | 89.50827 | 103.47914 | 91.02709 | 103.15637 | 93.17974 | 97.71045 | 92.46447 | 109.69384 | 102.30353 | 106.14076 | 69.421616 | 122.19086 | 115.02639 | 61.920643 | 85.82775 | 101.61512 | 79.21686 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:16:56 | 0.000 sec | 2 | .86E1 | 15.0 | 0.45211 | 0.452069 | 0.452399 | 0.01295 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:16:56 | 0.003 sec | 4 | .53E1 | 15.0 | 0.45067 | 0.450654 | 0.451023 | 0.012899 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:16:56 | 0.005 sec | 6 | .33E1 | 15.0 | 0.448406 | 0.448429 | 0.448858 | 0.012821 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:16:56 | 0.008 sec | 8 | .21E1 | 15.0 | 0.444887 | 0.444968 | 0.44549 | 0.012701 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:16:56 | 0.010 sec | 10 | .13E1 | 15.0 | 0.439585 | 0.439748 | 0.440403 | 0.012525 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:16:56 | 0.013 sec | 12 | .79E0 | 15.0 | 0.431944 | 0.432212 | 0.433043 | 0.012283 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:16:56 | 0.015 sec | 14 | .49E0 | 15.0 | 0.421769 | 0.422137 | 0.423177 | 0.011988 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:16:56 | 0.017 sec | 16 | .31E0 | 15.0 | 0.409871 | 0.410273 | 0.41153 | 0.011695 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:16:56 | 0.020 sec | 18 | .19E0 | 15.0 | 0.398221 | 0.398495 | 0.400003 | 0.011495 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:16:56 | 0.022 sec | 20 | .12E0 | 15.0 | 0.388804 | 0.38873 | 0.390635 | 0.011444 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:16:56 | 0.025 sec | 22 | .73E-1 | 15.0 | 0.382289 | 0.381678 | 0.384198 | 0.011523 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:16:57 | 0.028 sec | 24 | .45E-1 | 15.0 | 0.378213 | 0.376961 | 0.380273 | 0.011673 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:16:57 | 0.030 sec | 26 | .28E-1 | 15.0 | 0.375786 | 0.373881 | 0.378058 | 0.011843 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:16:57 | 0.033 sec | 28 | .18E-1 | 15.0 | 0.374357 | 0.371847 | 0.376384 | 0.011842 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:16:57 | 0.035 sec | 30 | .11E-1 | 15.0 | 0.373518 | 0.370484 | 0.375605 | 0.011918 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:16:57 | 0.038 sec | 32 | .68E-2 | 15.0 | 0.373032 | 0.369567 | 0.375187 | 0.011977 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:16:56 | 0.304 sec | 33 | None | NaN | 33.0 | 0.219391 | 0.186379 | 0.148024 | 0.770793 | 0.289212 | 8.318376 | 0.078217 | 0.218688 | 0.184477 | 0.153269 | 0.790554 | 0.294321 | 9.984615 | 0.07396 | ||||||
| 17 | 2021-07-15 20:16:57 | 0.041 sec | 34 | .42E-2 | 15.0 | 0.372759 | 0.368955 | 0.374981 | 0.012023 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:16:57 | 0.043 sec | 35 | .26E-2 | 15.0 | 0.372613 | 0.368555 | 0.379871 | 0.012669 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:16:57 | 0.044 sec | 36 | .16E-2 | 15.0 | 0.372535 | 0.368286 | 0.384164 | 0.013635 | 0.0 | NaN |
See the whole table with table.as_data_frame() Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.568888 | 1.000000 | 0.259244 |
| 1 | Average_Transaction_Frequency | 0.280105 | 0.492373 | 0.127645 |
| 2 | Merchant_ID | 0.205506 | 0.361243 | 0.093650 |
| 3 | Minimum_Transaction_Amount | 0.188753 | 0.331792 | 0.086015 |
| 4 | Card_Type.1 | 0.178540 | 0.313841 | 0.081361 |
| 5 | Channel_ID | 0.178035 | 0.312953 | 0.081131 |
| 6 | Card_Type.0 | 0.174502 | 0.306743 | 0.079521 |
| 7 | Maximum_Transaction_Amount | 0.107826 | 0.189539 | 0.049137 |
| 8 | Transaction_Amount | 0.106561 | 0.187314 | 0.048560 |
| 9 | Transaction_Date | 0.082682 | 0.145340 | 0.037679 |
| 10 | Average_Transaction_Amount | 0.043530 | 0.076517 | 0.019837 |
| 11 | Day | 0.032815 | 0.057682 | 0.014954 |
| 12 | Month | 0.027390 | 0.048146 | 0.012482 |
| 13 | City_ID | 0.019277 | 0.033885 | 0.008784 |
[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]
import statistics
totalF1 = []
for i in models:
F1s=i.F1(valid=True)[0][1]
totalF1.append(F1s)
a=statistics.mean(totalF1)
a
0.41995830294642694
totalthreshold = []
for i in models:
thresholds=i.F1(valid=True)[0][0]
totalthreshold.append(thresholds)
b=statistics.mean(totalthreshold)
b
0.19787958062573607
totalprecision = []
for i in models:
precisions=i.precision(valid=True)[0][1]
totalprecision.append(precisions)
c=statistics.mean(totalprecision)
c
0.8318639291465378
totalrecall = []
for i in models:
recalls=i.recall(valid=True)[0][1]
totalrecall.append(recalls)
d=statistics.mean(totalrecall)
d
1.0
totalaccuracy = []
for i in models:
accuracies=i.accuracy(valid=True)[0][1]
totalaccuracy.append(accuracies)
e=statistics.mean(totalaccuracy)
e
0.9417907892484164
f1 = a
threshold = b
precision = c
sensitivity = d
accuracy = e
metrics = {'Threshold': threshold, 'F1': f1, 'Precision': precision, 'Sensitivity': sensitivity, 'Accuracy': accuracy}
glm_performance = pd.DataFrame(metrics.values(), columns=['Value'], index=metrics.keys())
glm_performance
| Value | |
|---|---|
| Threshold | 0.197880 |
| F1 | 0.419958 |
| Precision | 0.831864 |
| Sensitivity | 1.000000 |
| Accuracy | 0.941791 |
base_model = GLM.leader
base_model
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201647 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.004202 ) | nlambda = 30, lambda.max = 8.5862, lambda.min = 0.004202, lambda.1... | 14 | 14 | 34 | automl_training_py_909_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04813232537278134 RMSE: 0.2193908051235998 LogLoss: 0.18637933900220385 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793846 Residual deviance: 2902.299066942318 AIC: 2932.299066942318 AUC: 0.7707934772706568 AUCPR: 0.289212154938732 Gini: 0.5415869545413137 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.14313113283612258:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6967.0 | 351.0 | 0.048 | (351.0/7318.0) |
| 1 | 1 | 258.0 | 210.0 | 0.5513 | (258.0/468.0) |
| 2 | Total | 7225.0 | 561.0 | 0.0782 | (609.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.143131 | 0.408163 | 185.0 |
| 1 | max f2 | 0.060570 | 0.439140 | 232.0 |
| 2 | max f0point5 | 0.354100 | 0.421651 | 100.0 |
| 3 | max accuracy | 0.558818 | 0.940791 | 15.0 |
| 4 | max precision | 0.862623 | 1.000000 | 0.0 |
| 5 | max recall | 0.017653 | 1.000000 | 381.0 |
| 6 | max specificity | 0.862623 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.281847 | 0.368653 | 148.0 |
| 8 | max min_per_class_accuracy | 0.040479 | 0.686526 | 288.0 |
| 9 | max mean_per_class_accuracy | 0.060570 | 0.714366 | 232.0 |
| 10 | max tns | 0.862623 | 7318.000000 | 0.0 |
| 11 | max fns | 0.862623 | 467.000000 | 0.0 |
| 12 | max fps | 0.000954 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017653 | 468.000000 | 381.0 |
| 14 | max tnr | 0.862623 | 1.000000 | 0.0 |
| 15 | max fnr | 0.862623 | 0.997863 | 0.0 |
| 16 | max fpr | 0.000954 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017653 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.432753 | 8.318376 | 8.318376 | 0.500000 | 0.523657 | 0.500000 | 0.523657 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.395496 | 8.105084 | 8.211730 | 0.487179 | 0.413156 | 0.493590 | 0.468407 | 0.081197 | 0.164530 | 710.508437 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.371288 | 6.825334 | 7.749598 | 0.410256 | 0.382717 | 0.465812 | 0.439843 | 0.068376 | 0.232906 | 582.533421 | 674.959822 | 0.215825 |
| 3 | 4 | 0.040072 | 0.351103 | 7.038626 | 7.571855 | 0.423077 | 0.361164 | 0.455128 | 0.420174 | 0.070513 | 0.303419 | 603.862590 | 657.185514 | 0.280188 |
| 4 | 5 | 0.050090 | 0.329267 | 4.692417 | 6.995968 | 0.282051 | 0.341378 | 0.420513 | 0.404415 | 0.047009 | 0.350427 | 369.241727 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.065548 | 2.822688 | 4.912006 | 0.169666 | 0.168446 | 0.295250 | 0.286582 | 0.141026 | 0.491453 | 182.268802 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.051579 | 1.154736 | 3.660655 | 0.069409 | 0.056876 | 0.220034 | 0.210079 | 0.057692 | 0.549145 | 15.473601 | 266.065522 | 0.424658 |
| 7 | 8 | 0.200103 | 0.046690 | 0.853167 | 2.957882 | 0.051282 | 0.048764 | 0.177792 | 0.169698 | 0.042735 | 0.591880 | -14.683322 | 195.788212 | 0.416833 |
| 8 | 9 | 0.300026 | 0.041670 | 0.748440 | 2.222032 | 0.044987 | 0.043930 | 0.133562 | 0.127811 | 0.074786 | 0.666667 | -25.155999 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.038420 | 0.662053 | 1.831912 | 0.039795 | 0.039922 | 0.110112 | 0.105832 | 0.066239 | 0.732906 | -33.794696 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.035857 | 0.620136 | 1.589744 | 0.037275 | 0.037116 | 0.095556 | 0.092099 | 0.061966 | 0.794872 | -37.986399 | 58.974359 | 0.313729 |
| 11 | 12 | 0.600051 | 0.033391 | 0.619340 | 1.427940 | 0.037227 | 0.034596 | 0.085830 | 0.082511 | 0.061966 | 0.856838 | -38.066006 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.030890 | 0.513216 | 1.297361 | 0.030848 | 0.032115 | 0.077982 | 0.075317 | 0.051282 | 0.908120 | -48.678400 | 29.736141 | 0.221457 |
| 13 | 14 | 0.800026 | 0.027827 | 0.384418 | 1.183189 | 0.023107 | 0.029418 | 0.071119 | 0.069577 | 0.038462 | 0.946581 | -61.558211 | 18.318850 | 0.155928 |
| 14 | 15 | 0.899949 | 0.023557 | 0.213840 | 1.075560 | 0.012853 | 0.025859 | 0.064650 | 0.064723 | 0.021368 | 0.967949 | -78.616000 | 7.555997 | 0.072349 |
| 15 | 16 | 1.000000 | 0.000731 | 0.320348 | 1.000000 | 0.019255 | 0.018596 | 0.060108 | 0.060108 | 0.032051 | 1.000000 | -67.965176 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04782450600267793 RMSE: 0.21868814783311402 LogLoss: 0.18447730336658244 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311447 Residual deviance: 718.3546193094721 AIC: 748.3546193094721 AUC: 0.7905539208817898 AUCPR: 0.29432069272826045 Gini: 0.5811078417635795 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.21146723168373893:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1752.0 | 78.0 | 0.0426 | (78.0/1830.0) |
| 1 | 1 | 66.0 | 51.0 | 0.5641 | (66.0/117.0) |
| 2 | Total | 1818.0 | 129.0 | 0.074 | (144.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.211467 | 0.414634 | 111.0 |
| 1 | max f2 | 0.076076 | 0.452418 | 149.0 |
| 2 | max f0point5 | 0.338572 | 0.442890 | 64.0 |
| 3 | max accuracy | 0.441536 | 0.942476 | 15.0 |
| 4 | max precision | 0.471693 | 0.666667 | 11.0 |
| 5 | max recall | 0.019966 | 1.000000 | 369.0 |
| 6 | max specificity | 0.582110 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.211467 | 0.375770 | 111.0 |
| 8 | max min_per_class_accuracy | 0.041158 | 0.716393 | 242.0 |
| 9 | max mean_per_class_accuracy | 0.040708 | 0.718166 | 244.0 |
| 10 | max tns | 0.582110 | 1829.000000 | 0.0 |
| 11 | max fns | 0.582110 | 117.000000 | 0.0 |
| 12 | max fps | 0.000962 | 1830.000000 | 399.0 |
| 13 | max tps | 0.019966 | 117.000000 | 369.0 |
| 14 | max tnr | 0.582110 | 0.999454 | 0.0 |
| 15 | max fnr | 0.582110 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000962 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019966 | 1.000000 | 369.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.85 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.425889 | 9.984615 | 9.984615 | 0.600000 | 0.481839 | 0.600000 | 0.481839 | 0.102564 | 0.102564 | 898.461538 | 898.461538 | 0.098193 |
| 1 | 2 | 0.020031 | 0.397049 | 6.130904 | 8.107166 | 0.368421 | 0.410298 | 0.487179 | 0.446986 | 0.059829 | 0.162393 | 513.090418 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.365072 | 7.488462 | 7.897436 | 0.450000 | 0.382049 | 0.474576 | 0.424973 | 0.076923 | 0.239316 | 648.846154 | 689.743590 | 0.222376 |
| 3 | 4 | 0.040062 | 0.337203 | 8.758435 | 8.107166 | 0.526316 | 0.351274 | 0.487179 | 0.407021 | 0.085470 | 0.324786 | 775.843455 | 710.716634 | 0.302928 |
| 4 | 5 | 0.050334 | 0.306861 | 4.160256 | 7.301675 | 0.250000 | 0.321644 | 0.438776 | 0.389597 | 0.042735 | 0.367521 | 316.025641 | 630.167452 | 0.337467 |
| 5 | 6 | 0.100154 | 0.066541 | 2.744911 | 5.034977 | 0.164948 | 0.157343 | 0.302564 | 0.274065 | 0.136752 | 0.504274 | 174.491145 | 403.497699 | 0.429957 |
| 6 | 7 | 0.149974 | 0.051031 | 0.343114 | 3.476379 | 0.020619 | 0.057350 | 0.208904 | 0.202074 | 0.017094 | 0.521368 | -65.688607 | 247.637864 | 0.395138 |
| 7 | 8 | 0.200308 | 0.046055 | 1.188645 | 2.901512 | 0.071429 | 0.048291 | 0.174359 | 0.163431 | 0.059829 | 0.581197 | 18.864469 | 190.151216 | 0.405240 |
| 8 | 9 | 0.299949 | 0.041386 | 1.200899 | 2.336582 | 0.072165 | 0.043356 | 0.140411 | 0.123543 | 0.119658 | 0.700855 | 20.089876 | 133.658237 | 0.426538 |
| 9 | 10 | 0.400103 | 0.037682 | 0.597370 | 1.901221 | 0.035897 | 0.039313 | 0.114249 | 0.102459 | 0.059829 | 0.760684 | -40.262985 | 90.122116 | 0.383635 |
| 10 | 11 | 0.500257 | 0.035470 | 0.938725 | 1.708524 | 0.056410 | 0.036502 | 0.102669 | 0.089254 | 0.094017 | 0.854701 | -6.127548 | 70.852419 | 0.377105 |
| 11 | 12 | 0.599897 | 0.033071 | 0.343114 | 1.481735 | 0.020619 | 0.034212 | 0.089041 | 0.080112 | 0.034188 | 0.888889 | -65.688607 | 48.173516 | 0.307468 |
| 12 | 13 | 0.700051 | 0.030565 | 0.341354 | 1.318585 | 0.020513 | 0.031775 | 0.079237 | 0.073196 | 0.034188 | 0.923077 | -65.864563 | 31.858457 | 0.237285 |
| 13 | 14 | 0.799692 | 0.027585 | 0.428892 | 1.207730 | 0.025773 | 0.029153 | 0.072575 | 0.067709 | 0.042735 | 0.965812 | -57.110759 | 20.773018 | 0.176741 |
| 14 | 15 | 0.899846 | 0.023553 | 0.170677 | 1.092305 | 0.010256 | 0.025682 | 0.065639 | 0.063031 | 0.017094 | 0.982906 | -82.932281 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.000808 | 0.170677 | 1.000000 | 0.010256 | 0.018189 | 0.060092 | 0.058540 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048314917844115436 RMSE: 0.21980654640868966 LogLoss: 0.1874930722653597 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.536621270318 Residual deviance: 2919.642121316181 AIC: 2949.642121316181 AUC: 0.7614112433222846 AUCPR: 0.2789306901875544 Gini: 0.5228224866445692 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2500729007254136:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7017.0 | 301.0 | 0.0411 | (301.0/7318.0) |
| 1 | 1 | 270.0 | 198.0 | 0.5769 | (270.0/468.0) |
| 2 | Total | 7287.0 | 499.0 | 0.0733 | (571.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.250073 | 0.409514 | 157.0 |
| 1 | max f2 | 0.060166 | 0.433100 | 234.0 |
| 2 | max f0point5 | 0.352674 | 0.408477 | 99.0 |
| 3 | max accuracy | 0.572231 | 0.940663 | 15.0 |
| 4 | max precision | 0.654390 | 0.714286 | 6.0 |
| 5 | max recall | 0.017309 | 1.000000 | 382.0 |
| 6 | max specificity | 0.877201 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.250073 | 0.370677 | 157.0 |
| 8 | max min_per_class_accuracy | 0.041276 | 0.685297 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.059909 | 0.712950 | 235.0 |
| 10 | max tns | 0.877201 | 7317.000000 | 0.0 |
| 11 | max fns | 0.877201 | 468.000000 | 0.0 |
| 12 | max fps | 0.000998 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017309 | 468.000000 | 382.0 |
| 14 | max tnr | 0.877201 | 0.999863 | 0.0 |
| 15 | max fnr | 0.877201 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000998 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017309 | 1.000000 | 382.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.99 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.431033 | 7.891793 | 7.891793 | 0.474359 | 0.519818 | 0.474359 | 0.519818 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.392287 | 8.105084 | 7.998439 | 0.487179 | 0.408719 | 0.480769 | 0.464269 | 0.081197 | 0.160256 | 710.508437 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.369043 | 6.612043 | 7.536307 | 0.397436 | 0.379414 | 0.452991 | 0.435984 | 0.066239 | 0.226496 | 561.204252 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.347586 | 6.825334 | 7.358563 | 0.410256 | 0.357128 | 0.442308 | 0.416270 | 0.068376 | 0.294872 | 582.533421 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.323940 | 4.692417 | 6.825334 | 0.282051 | 0.337127 | 0.410256 | 0.400441 | 0.047009 | 0.341880 | 369.241727 | 582.533421 | 0.310451 |
| 5 | 6 | 0.100051 | 0.063036 | 2.993760 | 4.912006 | 0.179949 | 0.153041 | 0.295250 | 0.276900 | 0.149573 | 0.491453 | 199.376002 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.056793 | 1.154736 | 3.660655 | 0.069409 | 0.059871 | 0.220034 | 0.204619 | 0.057692 | 0.549145 | 15.473601 | 266.065522 | 0.424658 |
| 7 | 8 | 0.200103 | 0.048869 | 0.682533 | 2.915169 | 0.041026 | 0.052080 | 0.175225 | 0.166435 | 0.034188 | 0.583333 | -31.746658 | 191.516902 | 0.407739 |
| 8 | 9 | 0.300026 | 0.042665 | 0.727056 | 2.186422 | 0.043702 | 0.045309 | 0.131421 | 0.126094 | 0.072650 | 0.655983 | -27.294399 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.039113 | 0.768836 | 1.831912 | 0.046213 | 0.040771 | 0.110112 | 0.104757 | 0.076923 | 0.732906 | -23.116421 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.036372 | 0.427680 | 1.551282 | 0.025707 | 0.037736 | 0.093244 | 0.091363 | 0.042735 | 0.775641 | -57.232000 | 55.128205 | 0.293269 |
| 11 | 12 | 0.600051 | 0.033734 | 0.640696 | 1.399453 | 0.038511 | 0.035049 | 0.084118 | 0.081973 | 0.064103 | 0.839744 | -35.930351 | 39.945282 | 0.255021 |
| 12 | 13 | 0.699974 | 0.031193 | 0.598752 | 1.285151 | 0.035990 | 0.032454 | 0.077248 | 0.074904 | 0.059829 | 0.899573 | -40.124800 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.028184 | 0.320348 | 1.164493 | 0.019255 | 0.029771 | 0.069995 | 0.069260 | 0.032051 | 0.931624 | -67.965176 | 16.449252 | 0.140014 |
| 14 | 15 | 0.899949 | 0.023828 | 0.342144 | 1.073186 | 0.020566 | 0.026185 | 0.064507 | 0.064477 | 0.034188 | 0.965812 | -65.785600 | 7.318567 | 0.070075 |
| 15 | 16 | 1.000000 | 0.000777 | 0.341705 | 1.000000 | 0.020539 | 0.018901 | 0.060108 | 0.059917 | 0.034188 | 1.000000 | -65.829521 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9319325 | 0.025578208 | 0.9115385 | 0.9 | 0.9576923 | 0.9461538 | 0.9269231 | 0.9 | 0.93846154 | 0.9076923 | 0.9269231 | 0.9346154 | 0.9307692 | 0.91923076 | 0.9346154 | 0.9576923 | 0.9307692 | 0.96153843 | 0.94208497 | 0.93050194 | 0.95366794 | 0.95366794 | 0.8957529 | 0.9266409 | 0.93436295 | 0.969112 | 0.84555984 | 0.9266409 | 0.95752895 | 0.96525097 | 0.9459459 | 0.9266409 |
| 1 | auc | 0.762986 | 0.07631634 | 0.72020835 | 0.7259227 | 0.7214312 | 0.7930283 | 0.9142023 | 0.7395896 | 0.7705156 | 0.7473531 | 0.83878 | 0.8833212 | 0.8249328 | 0.82575756 | 0.7522957 | 0.6680108 | 0.79253113 | 0.84581786 | 0.80935913 | 0.7336066 | 0.6644315 | 0.8130144 | 0.7408848 | 0.67721194 | 0.764177 | 0.8004016 | 0.6120564 | 0.8002092 | 0.87188756 | 0.6457161 | 0.62103826 | 0.77188754 |
| 2 | err | 0.06806752 | 0.025578208 | 0.08846154 | 0.1 | 0.042307694 | 0.053846154 | 0.073076926 | 0.1 | 0.06153846 | 0.092307694 | 0.073076926 | 0.06538462 | 0.06923077 | 0.08076923 | 0.06538462 | 0.042307694 | 0.06923077 | 0.03846154 | 0.057915058 | 0.06949807 | 0.046332046 | 0.046332046 | 0.1042471 | 0.07335907 | 0.06563707 | 0.03088803 | 0.15444015 | 0.07335907 | 0.042471044 | 0.034749035 | 0.054054055 | 0.07335907 |
| 3 | err_count | 17.666666 | 6.634982 | 23.0 | 26.0 | 11.0 | 14.0 | 19.0 | 26.0 | 16.0 | 24.0 | 19.0 | 17.0 | 18.0 | 21.0 | 17.0 | 11.0 | 18.0 | 10.0 | 15.0 | 18.0 | 12.0 | 12.0 | 27.0 | 19.0 | 17.0 | 8.0 | 40.0 | 19.0 | 11.0 | 9.0 | 14.0 | 19.0 |
| 4 | f0point5 | 0.46578673 | 0.12688833 | 0.39772728 | 0.37815127 | 0.42857143 | 0.57377046 | 0.39130434 | 0.3809524 | 0.5294118 | 0.21052632 | 0.46391752 | 0.53097343 | 0.38043478 | 0.44117647 | 0.51282054 | 0.46875 | 0.5263158 | 0.7746479 | 0.5714286 | 0.42168674 | 0.5263158 | 0.625 | 0.3305785 | 0.41666666 | 0.5 | 0.5952381 | 0.1910828 | 0.5 | 0.48387095 | 0.6756757 | 0.49019608 | 0.25641027 |
| 5 | f1 | 0.44847605 | 0.098334536 | 0.3783784 | 0.4090909 | 0.3529412 | 0.5 | 0.4864865 | 0.3809524 | 0.5294118 | 0.25 | 0.4864865 | 0.58536583 | 0.4375 | 0.46153846 | 0.4848485 | 0.3529412 | 0.5263158 | 0.6875 | 0.516129 | 0.4375 | 0.4 | 0.53846157 | 0.37209302 | 0.42424244 | 0.4516129 | 0.5555556 | 0.23076923 | 0.45714286 | 0.5217391 | 0.5263158 | 0.41666666 | 0.2962963 |
| 6 | f2 | 0.4464339 | 0.100452535 | 0.36082473 | 0.44554454 | 0.3 | 0.443038 | 0.64285713 | 0.3809524 | 0.5294118 | 0.30769232 | 0.5113636 | 0.65217394 | 0.5147059 | 0.48387095 | 0.4597701 | 0.28301886 | 0.5263158 | 0.6179775 | 0.47058824 | 0.45454547 | 0.32258064 | 0.47297296 | 0.42553192 | 0.43209878 | 0.4117647 | 0.5208333 | 0.29126215 | 0.42105263 | 0.5660377 | 0.43103448 | 0.36231884 | 0.3508772 |
| 7 | lift_top_group | 8.203134 | 4.896419 | 4.3333335 | 9.122807 | 7.878788 | 10.196078 | 7.878788 | 0.0 | 5.098039 | 0.0 | 5.098039 | 5.098039 | 0.0 | 9.62963 | 9.62963 | 14.444445 | 4.5614033 | 13.684211 | 9.592592 | 11.511111 | 6.1666665 | 16.1875 | 10.156863 | 5.3958335 | 9.592592 | 17.266666 | 0.0 | 8.633333 | 17.266666 | 13.282051 | 5.7555556 | 8.633333 |
| 8 | logloss | 0.18636669 | 0.03260542 | 0.24428856 | 0.22533733 | 0.15564056 | 0.18899742 | 0.12618236 | 0.2517394 | 0.18479693 | 0.16542803 | 0.18868947 | 0.16908133 | 0.15116997 | 0.19655243 | 0.21029659 | 0.17213129 | 0.19899835 | 0.1750521 | 0.19914357 | 0.17988367 | 0.18863021 | 0.17850283 | 0.21176417 | 0.19749716 | 0.20490494 | 0.13401856 | 0.23588969 | 0.22205867 | 0.11953792 | 0.16569065 | 0.19616818 | 0.1529283 |
| 9 | max_per_class_error | 0.54597044 | 0.123270094 | 0.65 | 0.5263158 | 0.72727275 | 0.5882353 | 0.18181819 | 0.61904764 | 0.47058824 | 0.6363636 | 0.47058824 | 0.29411766 | 0.41666666 | 0.5 | 0.5555556 | 0.75 | 0.47368422 | 0.42105263 | 0.5555556 | 0.53333336 | 0.71428573 | 0.5625 | 0.5294118 | 0.5625 | 0.6111111 | 0.5 | 0.64705884 | 0.6 | 0.4 | 0.61538464 | 0.6666667 | 0.6 |
| 10 | mcc | 0.4252677 | 0.10715261 | 0.33236364 | 0.35957506 | 0.34946984 | 0.4854702 | 0.5031643 | 0.32655907 | 0.49648994 | 0.21819456 | 0.44914004 | 0.56066114 | 0.4180421 | 0.41961062 | 0.45238268 | 0.36962467 | 0.48897138 | 0.6814499 | 0.49347085 | 0.4015085 | 0.41720933 | 0.5312636 | 0.32650575 | 0.3853074 | 0.4239339 | 0.5433689 | 0.16886064 | 0.42365196 | 0.5046277 | 0.5523514 | 0.40422782 | 0.270574 |
| 11 | mean_per_class_accuracy | 0.70827585 | 0.05993818 | 0.65416664 | 0.7036471 | 0.63033956 | 0.6976519 | 0.87495434 | 0.66327953 | 0.748245 | 0.64768165 | 0.74207217 | 0.8282498 | 0.765457 | 0.7252066 | 0.70775944 | 0.62096775 | 0.7444857 | 0.78532434 | 0.7118488 | 0.7128415 | 0.6387755 | 0.71257716 | 0.698104 | 0.6981739 | 0.6819963 | 0.7439759 | 0.61655325 | 0.68535566 | 0.78594375 | 0.6902752 | 0.65847 | 0.6738956 |
| 12 | mean_per_class_error | 0.29172418 | 0.05993818 | 0.34583333 | 0.29635292 | 0.36966047 | 0.3023481 | 0.12504564 | 0.33672047 | 0.25175503 | 0.35231838 | 0.25792786 | 0.17175019 | 0.23454301 | 0.2747934 | 0.2922406 | 0.37903225 | 0.2555143 | 0.2146757 | 0.28815123 | 0.28715846 | 0.3612245 | 0.28742284 | 0.30189598 | 0.30182612 | 0.31800368 | 0.2560241 | 0.38344675 | 0.31464434 | 0.21405622 | 0.30972484 | 0.34153005 | 0.32610443 |
| 13 | mse | 0.048063576 | 0.00965118 | 0.064496316 | 0.060373373 | 0.037184797 | 0.049575035 | 0.03253757 | 0.067704424 | 0.04758204 | 0.041225802 | 0.05088426 | 0.045837972 | 0.038932223 | 0.05288938 | 0.055764697 | 0.041286528 | 0.052850936 | 0.04594302 | 0.052345622 | 0.045467015 | 0.046493143 | 0.045737382 | 0.055761192 | 0.051214315 | 0.053853434 | 0.032400273 | 0.060440842 | 0.059060734 | 0.028494636 | 0.040120754 | 0.04884066 | 0.036608886 |
| 14 | null_deviance | 118.01789 | 18.576435 | 142.31174 | 136.7752 | 92.82863 | 125.73113 | 92.82863 | 147.85797 | 125.73113 | 92.82863 | 125.73113 | 125.73113 | 98.28862 | 131.24835 | 131.24835 | 98.28862 | 136.7752 | 136.7752 | 131.12575 | 114.60118 | 109.11213 | 120.09978 | 125.607956 | 120.09978 | 131.12575 | 87.25086 | 125.607956 | 142.19029 | 87.25086 | 103.63261 | 114.60118 | 87.25086 |
| 15 | pr_auc | 0.3057826 | 0.11474565 | 0.24940026 | 0.2337053 | 0.25733662 | 0.41292053 | 0.2826141 | 0.21975118 | 0.37333107 | 0.11041282 | 0.32695615 | 0.43757194 | 0.21647492 | 0.34615543 | 0.28245395 | 0.25142494 | 0.37477806 | 0.619697 | 0.43352246 | 0.3151317 | 0.26833844 | 0.49218583 | 0.23990041 | 0.2044688 | 0.32989296 | 0.32958278 | 0.11063523 | 0.37011787 | 0.44366908 | 0.32283646 | 0.18974203 | 0.1284695 |
| 16 | precision | 0.48855716 | 0.16440663 | 0.4117647 | 0.36 | 0.5 | 0.6363636 | 0.34615386 | 0.3809524 | 0.5294118 | 0.1904762 | 0.45 | 0.5 | 0.35 | 0.42857143 | 0.53333336 | 0.6 | 0.5263158 | 0.84615386 | 0.61538464 | 0.4117647 | 0.6666667 | 0.7 | 0.30769232 | 0.4117647 | 0.53846157 | 0.625 | 0.17142858 | 0.53333336 | 0.46153846 | 0.8333333 | 0.5555556 | 0.23529412 |
| 17 | r2 | 0.14220265 | 0.07521103 | 0.09167686 | 0.10870496 | 0.08225912 | 0.18875031 | 0.19695519 | 0.08810144 | 0.22136384 | -0.017475054 | 0.16732608 | 0.24990389 | 0.11565244 | 0.17921896 | 0.13459744 | 0.062174257 | 0.21975905 | 0.32174096 | 0.19054942 | 0.16667412 | 0.09072698 | 0.21087722 | 0.090783514 | 0.11638183 | 0.16723323 | 0.12713146 | 0.014479329 | 0.17116046 | 0.23234995 | 0.15843014 | 0.10484143 | 0.013750723 |
| 18 | recall | 0.45402953 | 0.123270094 | 0.35 | 0.47368422 | 0.27272728 | 0.4117647 | 0.8181818 | 0.3809524 | 0.5294118 | 0.36363637 | 0.5294118 | 0.7058824 | 0.5833333 | 0.5 | 0.44444445 | 0.25 | 0.5263158 | 0.57894737 | 0.44444445 | 0.46666667 | 0.2857143 | 0.4375 | 0.47058824 | 0.4375 | 0.3888889 | 0.5 | 0.3529412 | 0.4 | 0.6 | 0.3846154 | 0.33333334 | 0.4 |
| 19 | residual_deviance | 96.738235 | 16.93525 | 127.03005 | 117.17541 | 80.93309 | 98.278656 | 65.61483 | 130.9045 | 96.094406 | 86.022575 | 98.11853 | 87.922295 | 78.60838 | 102.20726 | 109.354225 | 89.50827 | 103.47914 | 91.02709 | 103.15637 | 93.17974 | 97.71045 | 92.46447 | 109.69384 | 102.30353 | 106.14076 | 69.421616 | 122.19086 | 115.02639 | 61.920643 | 85.82775 | 101.61512 | 79.21686 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:16:56 | 0.000 sec | 2 | .86E1 | 15.0 | 0.45211 | 0.452069 | 0.452399 | 0.01295 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:16:56 | 0.003 sec | 4 | .53E1 | 15.0 | 0.45067 | 0.450654 | 0.451023 | 0.012899 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:16:56 | 0.005 sec | 6 | .33E1 | 15.0 | 0.448406 | 0.448429 | 0.448858 | 0.012821 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:16:56 | 0.008 sec | 8 | .21E1 | 15.0 | 0.444887 | 0.444968 | 0.44549 | 0.012701 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:16:56 | 0.010 sec | 10 | .13E1 | 15.0 | 0.439585 | 0.439748 | 0.440403 | 0.012525 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:16:56 | 0.013 sec | 12 | .79E0 | 15.0 | 0.431944 | 0.432212 | 0.433043 | 0.012283 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:16:56 | 0.015 sec | 14 | .49E0 | 15.0 | 0.421769 | 0.422137 | 0.423177 | 0.011988 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:16:56 | 0.017 sec | 16 | .31E0 | 15.0 | 0.409871 | 0.410273 | 0.41153 | 0.011695 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:16:56 | 0.020 sec | 18 | .19E0 | 15.0 | 0.398221 | 0.398495 | 0.400003 | 0.011495 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:16:56 | 0.022 sec | 20 | .12E0 | 15.0 | 0.388804 | 0.38873 | 0.390635 | 0.011444 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:16:56 | 0.025 sec | 22 | .73E-1 | 15.0 | 0.382289 | 0.381678 | 0.384198 | 0.011523 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:16:57 | 0.028 sec | 24 | .45E-1 | 15.0 | 0.378213 | 0.376961 | 0.380273 | 0.011673 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:16:57 | 0.030 sec | 26 | .28E-1 | 15.0 | 0.375786 | 0.373881 | 0.378058 | 0.011843 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:16:57 | 0.033 sec | 28 | .18E-1 | 15.0 | 0.374357 | 0.371847 | 0.376384 | 0.011842 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:16:57 | 0.035 sec | 30 | .11E-1 | 15.0 | 0.373518 | 0.370484 | 0.375605 | 0.011918 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:16:57 | 0.038 sec | 32 | .68E-2 | 15.0 | 0.373032 | 0.369567 | 0.375187 | 0.011977 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:16:56 | 0.304 sec | 33 | None | NaN | 33.0 | 0.219391 | 0.186379 | 0.148024 | 0.770793 | 0.289212 | 8.318376 | 0.078217 | 0.218688 | 0.184477 | 0.153269 | 0.790554 | 0.294321 | 9.984615 | 0.07396 | ||||||
| 17 | 2021-07-15 20:16:57 | 0.041 sec | 34 | .42E-2 | 15.0 | 0.372759 | 0.368955 | 0.374981 | 0.012023 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:16:57 | 0.043 sec | 35 | .26E-2 | 15.0 | 0.372613 | 0.368555 | 0.379871 | 0.012669 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:16:57 | 0.044 sec | 36 | .16E-2 | 15.0 | 0.372535 | 0.368286 | 0.384164 | 0.013635 | 0.0 | NaN |
See the whole table with table.as_data_frame() Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.568888 | 1.000000 | 0.259244 |
| 1 | Average_Transaction_Frequency | 0.280105 | 0.492373 | 0.127645 |
| 2 | Merchant_ID | 0.205506 | 0.361243 | 0.093650 |
| 3 | Minimum_Transaction_Amount | 0.188753 | 0.331792 | 0.086015 |
| 4 | Card_Type.1 | 0.178540 | 0.313841 | 0.081361 |
| 5 | Channel_ID | 0.178035 | 0.312953 | 0.081131 |
| 6 | Card_Type.0 | 0.174502 | 0.306743 | 0.079521 |
| 7 | Maximum_Transaction_Amount | 0.107826 | 0.189539 | 0.049137 |
| 8 | Transaction_Amount | 0.106561 | 0.187314 | 0.048560 |
| 9 | Transaction_Date | 0.082682 | 0.145340 | 0.037679 |
| 10 | Average_Transaction_Amount | 0.043530 | 0.076517 | 0.019837 |
| 11 | Day | 0.032815 | 0.057682 | 0.014954 |
| 12 | Month | 0.027390 | 0.048146 | 0.012482 |
| 13 | City_ID | 0.019277 | 0.033885 | 0.008784 |
base_model.std_coef_plot(num_of_features=10)
def glm_coefficients(glm):
coefs = glm._model_json['output']['coefficients_table'].as_data_frame()
coefs['sign'] = np.where(coefs['standardized_coefficients'] < 0, 'Negative', 'Positive')
coefs['standardized_absolutes'] = coefs['standardized_coefficients'].abs()
coefs.sort_values(by='standardized_absolutes', ascending=False, inplace=True)
coefs.reset_index(drop=True, inplace=True)
return coefs
coefs_base_model = glm_coefficients(base_model)
coefs_base_model.head(20)
| names | coefficients | standardized_coefficients | sign | standardized_absolutes | |
|---|---|---|---|---|---|
| 0 | Intercept | 1.885480e+01 | -3.239034 | Negative | 3.239034 |
| 1 | Card_Holder | -2.270488e+00 | -0.568888 | Negative | 0.568888 |
| 2 | Average_Transaction_Frequency | -1.975786e-01 | -0.280105 | Negative | 0.280105 |
| 3 | Merchant_ID | 5.999282e-01 | 0.205506 | Positive | 0.205506 |
| 4 | Minimum_Transaction_Amount | 2.068984e-06 | 0.188753 | Positive | 0.188753 |
| 5 | Card_Type.1 | -1.785402e-01 | -0.178540 | Negative | 0.178540 |
| 6 | Channel_ID | -4.425433e-01 | -0.178035 | Negative | 0.178035 |
| 7 | Card_Type.0 | 1.745022e-01 | 0.174502 | Positive | 0.174502 |
| 8 | Maximum_Transaction_Amount | 6.430990e-09 | 0.107826 | Positive | 0.107826 |
| 9 | Transaction_Amount | 4.654538e-08 | 0.106561 | Positive | 0.106561 |
| 10 | Transaction_Date | -1.300652e-11 | -0.082682 | Negative | 0.082682 |
| 11 | Average_Transaction_Amount | 3.022511e-08 | 0.043530 | Positive | 0.043530 |
| 12 | Day | 1.653982e-02 | 0.032815 | Positive | 0.032815 |
| 13 | Month | -1.133285e-02 | -0.027390 | Negative | 0.027390 |
| 14 | City_ID | -3.921022e-02 | -0.019277 | Negative | 0.019277 |
def variable_importances(model, limit=0.95):
varimp = model.varimp(use_pandas=True)
varimp['cumulative'] = varimp['percentage'].cumsum()
varimp = varimp[varimp['cumulative'] <= limit].copy()
return varimp
varimp_base_model = variable_importances(base_model, limit=0.99)
varimp_base_model
| variable | relative_importance | scaled_importance | percentage | cumulative | |
|---|---|---|---|---|---|
| 0 | Card_Holder | 0.568888 | 1.000000 | 0.259244 | 0.259244 |
| 1 | Average_Transaction_Frequency | 0.280105 | 0.492373 | 0.127645 | 0.386889 |
| 2 | Merchant_ID | 0.205506 | 0.361243 | 0.093650 | 0.480539 |
| 3 | Minimum_Transaction_Amount | 0.188753 | 0.331792 | 0.086015 | 0.566554 |
| 4 | Card_Type.1 | 0.178540 | 0.313841 | 0.081361 | 0.647915 |
| 5 | Channel_ID | 0.178035 | 0.312953 | 0.081131 | 0.729047 |
| 6 | Card_Type.0 | 0.174502 | 0.306743 | 0.079521 | 0.808568 |
| 7 | Maximum_Transaction_Amount | 0.107826 | 0.189539 | 0.049137 | 0.857705 |
| 8 | Transaction_Amount | 0.106561 | 0.187314 | 0.048560 | 0.906265 |
| 9 | Transaction_Date | 0.082682 | 0.145340 | 0.037679 | 0.943944 |
| 10 | Average_Transaction_Amount | 0.043530 | 0.076517 | 0.019837 | 0.963780 |
| 11 | Day | 0.032815 | 0.057682 | 0.014954 | 0.978734 |
start_time = pd.to_datetime('now') + pd.Timedelta('07:00:00')
models=[]
for train, test in zip(trains, tests):
GBM = H2OAutoML(max_runtime_secs=300, nfolds=30, include_algos=['DRF'])
GBM.train(x=glm_vars, y='Fraud_Status', training_frame=train, validation_frame=test)
models.append(GBM.leader)
timer(start_time, header='\nModeling')
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20:46:37.669: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:47:39.28: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:48:43.327: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:49:53.725: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:50:57.432: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:52:03.778: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:53:14.246: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:54:30.561: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:55:38.595: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
20:56:48.853: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
20:57:57.741: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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20:59:07.160: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
21:00:15.552: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
21:01:24.31: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:02:10.351: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
21:02:57.506: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:03:49.894: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:04:47.24: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:05:48.354: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:06:51.427: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:07:57.575: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
21:09:14.671: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:10:35.143: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
███████████████████████████████████████████Failed polling AutoML progress log: Unexpected HTTP error: ('Connection aborted.', BadStatusLine('GET /3/Frames/AutoML_20210715_211035138_eventlog?row_count=10&row_offset=0&column_count=-1&full_column_count=-1&column_offset=0 HTTP/1.1\r\n'))
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21:11:52.575: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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21:13:05.732: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
21:14:36.888: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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AutoML progress: |
21:15:55.175: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
████████████████████████████████████████████████████████| 100%
AutoML progress: |
21:17:04.617: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
████████████████████████████████████████████████████████| 100%
AutoML progress: |
21:18:23.611: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
████████████████████████████████████████████████████████| 100%
AutoML progress: |
21:19:45.791: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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Modeling
Start : 2021-07-15 08:46:37 PM
Finish : 2021-07-15 09:20:57 PM
Runtime : 00:34:20
gbm = GLM.leader
gbm
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210715_201647 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.004202 ) | nlambda = 30, lambda.max = 8.5862, lambda.min = 0.004202, lambda.1... | 14 | 14 | 34 | automl_training_py_909_sid_b445 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04813232537278134 RMSE: 0.2193908051235998 LogLoss: 0.18637933900220385 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3538.959233793846 Residual deviance: 2902.299066942318 AIC: 2932.299066942318 AUC: 0.7707934772706568 AUCPR: 0.289212154938732 Gini: 0.5415869545413137 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.14313113283612258:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 6967.0 | 351.0 | 0.048 | (351.0/7318.0) |
| 1 | 1 | 258.0 | 210.0 | 0.5513 | (258.0/468.0) |
| 2 | Total | 7225.0 | 561.0 | 0.0782 | (609.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.143131 | 0.408163 | 185.0 |
| 1 | max f2 | 0.060570 | 0.439140 | 232.0 |
| 2 | max f0point5 | 0.354100 | 0.421651 | 100.0 |
| 3 | max accuracy | 0.558818 | 0.940791 | 15.0 |
| 4 | max precision | 0.862623 | 1.000000 | 0.0 |
| 5 | max recall | 0.017653 | 1.000000 | 381.0 |
| 6 | max specificity | 0.862623 | 1.000000 | 0.0 |
| 7 | max absolute_mcc | 0.281847 | 0.368653 | 148.0 |
| 8 | max min_per_class_accuracy | 0.040479 | 0.686526 | 288.0 |
| 9 | max mean_per_class_accuracy | 0.060570 | 0.714366 | 232.0 |
| 10 | max tns | 0.862623 | 7318.000000 | 0.0 |
| 11 | max fns | 0.862623 | 467.000000 | 0.0 |
| 12 | max fps | 0.000954 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017653 | 468.000000 | 381.0 |
| 14 | max tnr | 0.862623 | 1.000000 | 0.0 |
| 15 | max fnr | 0.862623 | 0.997863 | 0.0 |
| 16 | max fpr | 0.000954 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017653 | 1.000000 | 381.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.432753 | 8.318376 | 8.318376 | 0.500000 | 0.523657 | 0.500000 | 0.523657 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.395496 | 8.105084 | 8.211730 | 0.487179 | 0.413156 | 0.493590 | 0.468407 | 0.081197 | 0.164530 | 710.508437 | 721.173022 | 0.153735 |
| 2 | 3 | 0.030054 | 0.371288 | 6.825334 | 7.749598 | 0.410256 | 0.382717 | 0.465812 | 0.439843 | 0.068376 | 0.232906 | 582.533421 | 674.959822 | 0.215825 |
| 3 | 4 | 0.040072 | 0.351103 | 7.038626 | 7.571855 | 0.423077 | 0.361164 | 0.455128 | 0.420174 | 0.070513 | 0.303419 | 603.862590 | 657.185514 | 0.280188 |
| 4 | 5 | 0.050090 | 0.329267 | 4.692417 | 6.995968 | 0.282051 | 0.341378 | 0.420513 | 0.404415 | 0.047009 | 0.350427 | 369.241727 | 599.596757 | 0.319545 |
| 5 | 6 | 0.100051 | 0.065548 | 2.822688 | 4.912006 | 0.169666 | 0.168446 | 0.295250 | 0.286582 | 0.141026 | 0.491453 | 182.268802 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.051579 | 1.154736 | 3.660655 | 0.069409 | 0.056876 | 0.220034 | 0.210079 | 0.057692 | 0.549145 | 15.473601 | 266.065522 | 0.424658 |
| 7 | 8 | 0.200103 | 0.046690 | 0.853167 | 2.957882 | 0.051282 | 0.048764 | 0.177792 | 0.169698 | 0.042735 | 0.591880 | -14.683322 | 195.788212 | 0.416833 |
| 8 | 9 | 0.300026 | 0.041670 | 0.748440 | 2.222032 | 0.044987 | 0.043930 | 0.133562 | 0.127811 | 0.074786 | 0.666667 | -25.155999 | 122.203196 | 0.390088 |
| 9 | 10 | 0.400077 | 0.038420 | 0.662053 | 1.831912 | 0.039795 | 0.039922 | 0.110112 | 0.105832 | 0.066239 | 0.732906 | -33.794696 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.035857 | 0.620136 | 1.589744 | 0.037275 | 0.037116 | 0.095556 | 0.092099 | 0.061966 | 0.794872 | -37.986399 | 58.974359 | 0.313729 |
| 11 | 12 | 0.600051 | 0.033391 | 0.619340 | 1.427940 | 0.037227 | 0.034596 | 0.085830 | 0.082511 | 0.061966 | 0.856838 | -38.066006 | 42.794041 | 0.273208 |
| 12 | 13 | 0.699974 | 0.030890 | 0.513216 | 1.297361 | 0.030848 | 0.032115 | 0.077982 | 0.075317 | 0.051282 | 0.908120 | -48.678400 | 29.736141 | 0.221457 |
| 13 | 14 | 0.800026 | 0.027827 | 0.384418 | 1.183189 | 0.023107 | 0.029418 | 0.071119 | 0.069577 | 0.038462 | 0.946581 | -61.558211 | 18.318850 | 0.155928 |
| 14 | 15 | 0.899949 | 0.023557 | 0.213840 | 1.075560 | 0.012853 | 0.025859 | 0.064650 | 0.064723 | 0.021368 | 0.967949 | -78.616000 | 7.555997 | 0.072349 |
| 15 | 16 | 1.000000 | 0.000731 | 0.320348 | 1.000000 | 0.019255 | 0.018596 | 0.060108 | 0.060108 | 0.032051 | 1.000000 | -67.965176 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04782450600267793 RMSE: 0.21868814783311402 LogLoss: 0.18447730336658244 Null degrees of freedom: 1946 Residual degrees of freedom: 1932 Null deviance: 884.8017986311447 Residual deviance: 718.3546193094721 AIC: 748.3546193094721 AUC: 0.7905539208817898 AUCPR: 0.29432069272826045 Gini: 0.5811078417635795 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.21146723168373893:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1752.0 | 78.0 | 0.0426 | (78.0/1830.0) |
| 1 | 1 | 66.0 | 51.0 | 0.5641 | (66.0/117.0) |
| 2 | Total | 1818.0 | 129.0 | 0.074 | (144.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.211467 | 0.414634 | 111.0 |
| 1 | max f2 | 0.076076 | 0.452418 | 149.0 |
| 2 | max f0point5 | 0.338572 | 0.442890 | 64.0 |
| 3 | max accuracy | 0.441536 | 0.942476 | 15.0 |
| 4 | max precision | 0.471693 | 0.666667 | 11.0 |
| 5 | max recall | 0.019966 | 1.000000 | 369.0 |
| 6 | max specificity | 0.582110 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.211467 | 0.375770 | 111.0 |
| 8 | max min_per_class_accuracy | 0.041158 | 0.716393 | 242.0 |
| 9 | max mean_per_class_accuracy | 0.040708 | 0.718166 | 244.0 |
| 10 | max tns | 0.582110 | 1829.000000 | 0.0 |
| 11 | max fns | 0.582110 | 117.000000 | 0.0 |
| 12 | max fps | 0.000962 | 1830.000000 | 399.0 |
| 13 | max tps | 0.019966 | 117.000000 | 369.0 |
| 14 | max tnr | 0.582110 | 0.999454 | 0.0 |
| 15 | max fnr | 0.582110 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000962 | 1.000000 | 399.0 |
| 17 | max tpr | 0.019966 | 1.000000 | 369.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.85 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.425889 | 9.984615 | 9.984615 | 0.600000 | 0.481839 | 0.600000 | 0.481839 | 0.102564 | 0.102564 | 898.461538 | 898.461538 | 0.098193 |
| 1 | 2 | 0.020031 | 0.397049 | 6.130904 | 8.107166 | 0.368421 | 0.410298 | 0.487179 | 0.446986 | 0.059829 | 0.162393 | 513.090418 | 710.716634 | 0.151464 |
| 2 | 3 | 0.030303 | 0.365072 | 7.488462 | 7.897436 | 0.450000 | 0.382049 | 0.474576 | 0.424973 | 0.076923 | 0.239316 | 648.846154 | 689.743590 | 0.222376 |
| 3 | 4 | 0.040062 | 0.337203 | 8.758435 | 8.107166 | 0.526316 | 0.351274 | 0.487179 | 0.407021 | 0.085470 | 0.324786 | 775.843455 | 710.716634 | 0.302928 |
| 4 | 5 | 0.050334 | 0.306861 | 4.160256 | 7.301675 | 0.250000 | 0.321644 | 0.438776 | 0.389597 | 0.042735 | 0.367521 | 316.025641 | 630.167452 | 0.337467 |
| 5 | 6 | 0.100154 | 0.066541 | 2.744911 | 5.034977 | 0.164948 | 0.157343 | 0.302564 | 0.274065 | 0.136752 | 0.504274 | 174.491145 | 403.497699 | 0.429957 |
| 6 | 7 | 0.149974 | 0.051031 | 0.343114 | 3.476379 | 0.020619 | 0.057350 | 0.208904 | 0.202074 | 0.017094 | 0.521368 | -65.688607 | 247.637864 | 0.395138 |
| 7 | 8 | 0.200308 | 0.046055 | 1.188645 | 2.901512 | 0.071429 | 0.048291 | 0.174359 | 0.163431 | 0.059829 | 0.581197 | 18.864469 | 190.151216 | 0.405240 |
| 8 | 9 | 0.299949 | 0.041386 | 1.200899 | 2.336582 | 0.072165 | 0.043356 | 0.140411 | 0.123543 | 0.119658 | 0.700855 | 20.089876 | 133.658237 | 0.426538 |
| 9 | 10 | 0.400103 | 0.037682 | 0.597370 | 1.901221 | 0.035897 | 0.039313 | 0.114249 | 0.102459 | 0.059829 | 0.760684 | -40.262985 | 90.122116 | 0.383635 |
| 10 | 11 | 0.500257 | 0.035470 | 0.938725 | 1.708524 | 0.056410 | 0.036502 | 0.102669 | 0.089254 | 0.094017 | 0.854701 | -6.127548 | 70.852419 | 0.377105 |
| 11 | 12 | 0.599897 | 0.033071 | 0.343114 | 1.481735 | 0.020619 | 0.034212 | 0.089041 | 0.080112 | 0.034188 | 0.888889 | -65.688607 | 48.173516 | 0.307468 |
| 12 | 13 | 0.700051 | 0.030565 | 0.341354 | 1.318585 | 0.020513 | 0.031775 | 0.079237 | 0.073196 | 0.034188 | 0.923077 | -65.864563 | 31.858457 | 0.237285 |
| 13 | 14 | 0.799692 | 0.027585 | 0.428892 | 1.207730 | 0.025773 | 0.029153 | 0.072575 | 0.067709 | 0.042735 | 0.965812 | -57.110759 | 20.773018 | 0.176741 |
| 14 | 15 | 0.899846 | 0.023553 | 0.170677 | 1.092305 | 0.010256 | 0.025682 | 0.065639 | 0.063031 | 0.017094 | 0.982906 | -82.932281 | 9.230477 | 0.088370 |
| 15 | 16 | 1.000000 | 0.000808 | 0.170677 | 1.000000 | 0.010256 | 0.018189 | 0.060092 | 0.058540 | 0.017094 | 1.000000 | -82.932281 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.048314917844115436 RMSE: 0.21980654640868966 LogLoss: 0.1874930722653597 Null degrees of freedom: 7785 Residual degrees of freedom: 7771 Null deviance: 3540.536621270318 Residual deviance: 2919.642121316181 AIC: 2949.642121316181 AUC: 0.7614112433222846 AUCPR: 0.2789306901875544 Gini: 0.5228224866445692 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2500729007254136:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7017.0 | 301.0 | 0.0411 | (301.0/7318.0) |
| 1 | 1 | 270.0 | 198.0 | 0.5769 | (270.0/468.0) |
| 2 | Total | 7287.0 | 499.0 | 0.0733 | (571.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.250073 | 0.409514 | 157.0 |
| 1 | max f2 | 0.060166 | 0.433100 | 234.0 |
| 2 | max f0point5 | 0.352674 | 0.408477 | 99.0 |
| 3 | max accuracy | 0.572231 | 0.940663 | 15.0 |
| 4 | max precision | 0.654390 | 0.714286 | 6.0 |
| 5 | max recall | 0.017309 | 1.000000 | 382.0 |
| 6 | max specificity | 0.877201 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.250073 | 0.370677 | 157.0 |
| 8 | max min_per_class_accuracy | 0.041276 | 0.685297 | 286.0 |
| 9 | max mean_per_class_accuracy | 0.059909 | 0.712950 | 235.0 |
| 10 | max tns | 0.877201 | 7317.000000 | 0.0 |
| 11 | max fns | 0.877201 | 468.000000 | 0.0 |
| 12 | max fps | 0.000998 | 7318.000000 | 399.0 |
| 13 | max tps | 0.017309 | 468.000000 | 382.0 |
| 14 | max tnr | 0.877201 | 0.999863 | 0.0 |
| 15 | max fnr | 0.877201 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000998 | 1.000000 | 399.0 |
| 17 | max tpr | 0.017309 | 1.000000 | 382.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 5.99 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.431033 | 7.891793 | 7.891793 | 0.474359 | 0.519818 | 0.474359 | 0.519818 | 0.079060 | 0.079060 | 689.179268 | 689.179268 | 0.073457 |
| 1 | 2 | 0.020036 | 0.392287 | 8.105084 | 7.998439 | 0.487179 | 0.408719 | 0.480769 | 0.464269 | 0.081197 | 0.160256 | 710.508437 | 699.843853 | 0.149188 |
| 2 | 3 | 0.030054 | 0.369043 | 6.612043 | 7.536307 | 0.397436 | 0.379414 | 0.452991 | 0.435984 | 0.066239 | 0.226496 | 561.204252 | 653.630652 | 0.209005 |
| 3 | 4 | 0.040072 | 0.347586 | 6.825334 | 7.358563 | 0.410256 | 0.357128 | 0.442308 | 0.416270 | 0.068376 | 0.294872 | 582.533421 | 635.856345 | 0.271095 |
| 4 | 5 | 0.050090 | 0.323940 | 4.692417 | 6.825334 | 0.282051 | 0.337127 | 0.410256 | 0.400441 | 0.047009 | 0.341880 | 369.241727 | 582.533421 | 0.310451 |
| 5 | 6 | 0.100051 | 0.063036 | 2.993760 | 4.912006 | 0.179949 | 0.153041 | 0.295250 | 0.276900 | 0.149573 | 0.491453 | 199.376002 | 391.200641 | 0.416432 |
| 6 | 7 | 0.150013 | 0.056793 | 1.154736 | 3.660655 | 0.069409 | 0.059871 | 0.220034 | 0.204619 | 0.057692 | 0.549145 | 15.473601 | 266.065522 | 0.424658 |
| 7 | 8 | 0.200103 | 0.048869 | 0.682533 | 2.915169 | 0.041026 | 0.052080 | 0.175225 | 0.166435 | 0.034188 | 0.583333 | -31.746658 | 191.516902 | 0.407739 |
| 8 | 9 | 0.300026 | 0.042665 | 0.727056 | 2.186422 | 0.043702 | 0.045309 | 0.131421 | 0.126094 | 0.072650 | 0.655983 | -27.294399 | 118.642248 | 0.378721 |
| 9 | 10 | 0.400077 | 0.039113 | 0.768836 | 1.831912 | 0.046213 | 0.040771 | 0.110112 | 0.104757 | 0.076923 | 0.732906 | -23.116421 | 83.191203 | 0.354114 |
| 10 | 11 | 0.500000 | 0.036372 | 0.427680 | 1.551282 | 0.025707 | 0.037736 | 0.093244 | 0.091363 | 0.042735 | 0.775641 | -57.232000 | 55.128205 | 0.293269 |
| 11 | 12 | 0.600051 | 0.033734 | 0.640696 | 1.399453 | 0.038511 | 0.035049 | 0.084118 | 0.081973 | 0.064103 | 0.839744 | -35.930351 | 39.945282 | 0.255021 |
| 12 | 13 | 0.699974 | 0.031193 | 0.598752 | 1.285151 | 0.035990 | 0.032454 | 0.077248 | 0.074904 | 0.059829 | 0.899573 | -40.124800 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.028184 | 0.320348 | 1.164493 | 0.019255 | 0.029771 | 0.069995 | 0.069260 | 0.032051 | 0.931624 | -67.965176 | 16.449252 | 0.140014 |
| 14 | 15 | 0.899949 | 0.023828 | 0.342144 | 1.073186 | 0.020566 | 0.026185 | 0.064507 | 0.064477 | 0.034188 | 0.965812 | -65.785600 | 7.318567 | 0.070075 |
| 15 | 16 | 1.000000 | 0.000777 | 0.341705 | 1.000000 | 0.020539 | 0.018901 | 0.060108 | 0.059917 | 0.034188 | 1.000000 | -65.829521 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | cv_11_valid | cv_12_valid | cv_13_valid | cv_14_valid | cv_15_valid | cv_16_valid | cv_17_valid | cv_18_valid | cv_19_valid | cv_20_valid | cv_21_valid | cv_22_valid | cv_23_valid | cv_24_valid | cv_25_valid | cv_26_valid | cv_27_valid | cv_28_valid | cv_29_valid | cv_30_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.9319325 | 0.025578208 | 0.9115385 | 0.9 | 0.9576923 | 0.9461538 | 0.9269231 | 0.9 | 0.93846154 | 0.9076923 | 0.9269231 | 0.9346154 | 0.9307692 | 0.91923076 | 0.9346154 | 0.9576923 | 0.9307692 | 0.96153843 | 0.94208497 | 0.93050194 | 0.95366794 | 0.95366794 | 0.8957529 | 0.9266409 | 0.93436295 | 0.969112 | 0.84555984 | 0.9266409 | 0.95752895 | 0.96525097 | 0.9459459 | 0.9266409 |
| 1 | auc | 0.762986 | 0.07631634 | 0.72020835 | 0.7259227 | 0.7214312 | 0.7930283 | 0.9142023 | 0.7395896 | 0.7705156 | 0.7473531 | 0.83878 | 0.8833212 | 0.8249328 | 0.82575756 | 0.7522957 | 0.6680108 | 0.79253113 | 0.84581786 | 0.80935913 | 0.7336066 | 0.6644315 | 0.8130144 | 0.7408848 | 0.67721194 | 0.764177 | 0.8004016 | 0.6120564 | 0.8002092 | 0.87188756 | 0.6457161 | 0.62103826 | 0.77188754 |
| 2 | err | 0.06806752 | 0.025578208 | 0.08846154 | 0.1 | 0.042307694 | 0.053846154 | 0.073076926 | 0.1 | 0.06153846 | 0.092307694 | 0.073076926 | 0.06538462 | 0.06923077 | 0.08076923 | 0.06538462 | 0.042307694 | 0.06923077 | 0.03846154 | 0.057915058 | 0.06949807 | 0.046332046 | 0.046332046 | 0.1042471 | 0.07335907 | 0.06563707 | 0.03088803 | 0.15444015 | 0.07335907 | 0.042471044 | 0.034749035 | 0.054054055 | 0.07335907 |
| 3 | err_count | 17.666666 | 6.634982 | 23.0 | 26.0 | 11.0 | 14.0 | 19.0 | 26.0 | 16.0 | 24.0 | 19.0 | 17.0 | 18.0 | 21.0 | 17.0 | 11.0 | 18.0 | 10.0 | 15.0 | 18.0 | 12.0 | 12.0 | 27.0 | 19.0 | 17.0 | 8.0 | 40.0 | 19.0 | 11.0 | 9.0 | 14.0 | 19.0 |
| 4 | f0point5 | 0.46578673 | 0.12688833 | 0.39772728 | 0.37815127 | 0.42857143 | 0.57377046 | 0.39130434 | 0.3809524 | 0.5294118 | 0.21052632 | 0.46391752 | 0.53097343 | 0.38043478 | 0.44117647 | 0.51282054 | 0.46875 | 0.5263158 | 0.7746479 | 0.5714286 | 0.42168674 | 0.5263158 | 0.625 | 0.3305785 | 0.41666666 | 0.5 | 0.5952381 | 0.1910828 | 0.5 | 0.48387095 | 0.6756757 | 0.49019608 | 0.25641027 |
| 5 | f1 | 0.44847605 | 0.098334536 | 0.3783784 | 0.4090909 | 0.3529412 | 0.5 | 0.4864865 | 0.3809524 | 0.5294118 | 0.25 | 0.4864865 | 0.58536583 | 0.4375 | 0.46153846 | 0.4848485 | 0.3529412 | 0.5263158 | 0.6875 | 0.516129 | 0.4375 | 0.4 | 0.53846157 | 0.37209302 | 0.42424244 | 0.4516129 | 0.5555556 | 0.23076923 | 0.45714286 | 0.5217391 | 0.5263158 | 0.41666666 | 0.2962963 |
| 6 | f2 | 0.4464339 | 0.100452535 | 0.36082473 | 0.44554454 | 0.3 | 0.443038 | 0.64285713 | 0.3809524 | 0.5294118 | 0.30769232 | 0.5113636 | 0.65217394 | 0.5147059 | 0.48387095 | 0.4597701 | 0.28301886 | 0.5263158 | 0.6179775 | 0.47058824 | 0.45454547 | 0.32258064 | 0.47297296 | 0.42553192 | 0.43209878 | 0.4117647 | 0.5208333 | 0.29126215 | 0.42105263 | 0.5660377 | 0.43103448 | 0.36231884 | 0.3508772 |
| 7 | lift_top_group | 8.203134 | 4.896419 | 4.3333335 | 9.122807 | 7.878788 | 10.196078 | 7.878788 | 0.0 | 5.098039 | 0.0 | 5.098039 | 5.098039 | 0.0 | 9.62963 | 9.62963 | 14.444445 | 4.5614033 | 13.684211 | 9.592592 | 11.511111 | 6.1666665 | 16.1875 | 10.156863 | 5.3958335 | 9.592592 | 17.266666 | 0.0 | 8.633333 | 17.266666 | 13.282051 | 5.7555556 | 8.633333 |
| 8 | logloss | 0.18636669 | 0.03260542 | 0.24428856 | 0.22533733 | 0.15564056 | 0.18899742 | 0.12618236 | 0.2517394 | 0.18479693 | 0.16542803 | 0.18868947 | 0.16908133 | 0.15116997 | 0.19655243 | 0.21029659 | 0.17213129 | 0.19899835 | 0.1750521 | 0.19914357 | 0.17988367 | 0.18863021 | 0.17850283 | 0.21176417 | 0.19749716 | 0.20490494 | 0.13401856 | 0.23588969 | 0.22205867 | 0.11953792 | 0.16569065 | 0.19616818 | 0.1529283 |
| 9 | max_per_class_error | 0.54597044 | 0.123270094 | 0.65 | 0.5263158 | 0.72727275 | 0.5882353 | 0.18181819 | 0.61904764 | 0.47058824 | 0.6363636 | 0.47058824 | 0.29411766 | 0.41666666 | 0.5 | 0.5555556 | 0.75 | 0.47368422 | 0.42105263 | 0.5555556 | 0.53333336 | 0.71428573 | 0.5625 | 0.5294118 | 0.5625 | 0.6111111 | 0.5 | 0.64705884 | 0.6 | 0.4 | 0.61538464 | 0.6666667 | 0.6 |
| 10 | mcc | 0.4252677 | 0.10715261 | 0.33236364 | 0.35957506 | 0.34946984 | 0.4854702 | 0.5031643 | 0.32655907 | 0.49648994 | 0.21819456 | 0.44914004 | 0.56066114 | 0.4180421 | 0.41961062 | 0.45238268 | 0.36962467 | 0.48897138 | 0.6814499 | 0.49347085 | 0.4015085 | 0.41720933 | 0.5312636 | 0.32650575 | 0.3853074 | 0.4239339 | 0.5433689 | 0.16886064 | 0.42365196 | 0.5046277 | 0.5523514 | 0.40422782 | 0.270574 |
| 11 | mean_per_class_accuracy | 0.70827585 | 0.05993818 | 0.65416664 | 0.7036471 | 0.63033956 | 0.6976519 | 0.87495434 | 0.66327953 | 0.748245 | 0.64768165 | 0.74207217 | 0.8282498 | 0.765457 | 0.7252066 | 0.70775944 | 0.62096775 | 0.7444857 | 0.78532434 | 0.7118488 | 0.7128415 | 0.6387755 | 0.71257716 | 0.698104 | 0.6981739 | 0.6819963 | 0.7439759 | 0.61655325 | 0.68535566 | 0.78594375 | 0.6902752 | 0.65847 | 0.6738956 |
| 12 | mean_per_class_error | 0.29172418 | 0.05993818 | 0.34583333 | 0.29635292 | 0.36966047 | 0.3023481 | 0.12504564 | 0.33672047 | 0.25175503 | 0.35231838 | 0.25792786 | 0.17175019 | 0.23454301 | 0.2747934 | 0.2922406 | 0.37903225 | 0.2555143 | 0.2146757 | 0.28815123 | 0.28715846 | 0.3612245 | 0.28742284 | 0.30189598 | 0.30182612 | 0.31800368 | 0.2560241 | 0.38344675 | 0.31464434 | 0.21405622 | 0.30972484 | 0.34153005 | 0.32610443 |
| 13 | mse | 0.048063576 | 0.00965118 | 0.064496316 | 0.060373373 | 0.037184797 | 0.049575035 | 0.03253757 | 0.067704424 | 0.04758204 | 0.041225802 | 0.05088426 | 0.045837972 | 0.038932223 | 0.05288938 | 0.055764697 | 0.041286528 | 0.052850936 | 0.04594302 | 0.052345622 | 0.045467015 | 0.046493143 | 0.045737382 | 0.055761192 | 0.051214315 | 0.053853434 | 0.032400273 | 0.060440842 | 0.059060734 | 0.028494636 | 0.040120754 | 0.04884066 | 0.036608886 |
| 14 | null_deviance | 118.01789 | 18.576435 | 142.31174 | 136.7752 | 92.82863 | 125.73113 | 92.82863 | 147.85797 | 125.73113 | 92.82863 | 125.73113 | 125.73113 | 98.28862 | 131.24835 | 131.24835 | 98.28862 | 136.7752 | 136.7752 | 131.12575 | 114.60118 | 109.11213 | 120.09978 | 125.607956 | 120.09978 | 131.12575 | 87.25086 | 125.607956 | 142.19029 | 87.25086 | 103.63261 | 114.60118 | 87.25086 |
| 15 | pr_auc | 0.3057826 | 0.11474565 | 0.24940026 | 0.2337053 | 0.25733662 | 0.41292053 | 0.2826141 | 0.21975118 | 0.37333107 | 0.11041282 | 0.32695615 | 0.43757194 | 0.21647492 | 0.34615543 | 0.28245395 | 0.25142494 | 0.37477806 | 0.619697 | 0.43352246 | 0.3151317 | 0.26833844 | 0.49218583 | 0.23990041 | 0.2044688 | 0.32989296 | 0.32958278 | 0.11063523 | 0.37011787 | 0.44366908 | 0.32283646 | 0.18974203 | 0.1284695 |
| 16 | precision | 0.48855716 | 0.16440663 | 0.4117647 | 0.36 | 0.5 | 0.6363636 | 0.34615386 | 0.3809524 | 0.5294118 | 0.1904762 | 0.45 | 0.5 | 0.35 | 0.42857143 | 0.53333336 | 0.6 | 0.5263158 | 0.84615386 | 0.61538464 | 0.4117647 | 0.6666667 | 0.7 | 0.30769232 | 0.4117647 | 0.53846157 | 0.625 | 0.17142858 | 0.53333336 | 0.46153846 | 0.8333333 | 0.5555556 | 0.23529412 |
| 17 | r2 | 0.14220265 | 0.07521103 | 0.09167686 | 0.10870496 | 0.08225912 | 0.18875031 | 0.19695519 | 0.08810144 | 0.22136384 | -0.017475054 | 0.16732608 | 0.24990389 | 0.11565244 | 0.17921896 | 0.13459744 | 0.062174257 | 0.21975905 | 0.32174096 | 0.19054942 | 0.16667412 | 0.09072698 | 0.21087722 | 0.090783514 | 0.11638183 | 0.16723323 | 0.12713146 | 0.014479329 | 0.17116046 | 0.23234995 | 0.15843014 | 0.10484143 | 0.013750723 |
| 18 | recall | 0.45402953 | 0.123270094 | 0.35 | 0.47368422 | 0.27272728 | 0.4117647 | 0.8181818 | 0.3809524 | 0.5294118 | 0.36363637 | 0.5294118 | 0.7058824 | 0.5833333 | 0.5 | 0.44444445 | 0.25 | 0.5263158 | 0.57894737 | 0.44444445 | 0.46666667 | 0.2857143 | 0.4375 | 0.47058824 | 0.4375 | 0.3888889 | 0.5 | 0.3529412 | 0.4 | 0.6 | 0.3846154 | 0.33333334 | 0.4 |
| 19 | residual_deviance | 96.738235 | 16.93525 | 127.03005 | 117.17541 | 80.93309 | 98.278656 | 65.61483 | 130.9045 | 96.094406 | 86.022575 | 98.11853 | 87.922295 | 78.60838 | 102.20726 | 109.354225 | 89.50827 | 103.47914 | 91.02709 | 103.15637 | 93.17974 | 97.71045 | 92.46447 | 109.69384 | 102.30353 | 106.14076 | 69.421616 | 122.19086 | 115.02639 | 61.920643 | 85.82775 | 101.61512 | 79.21686 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-15 20:16:56 | 0.000 sec | 2 | .86E1 | 15.0 | 0.45211 | 0.452069 | 0.452399 | 0.01295 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-15 20:16:56 | 0.003 sec | 4 | .53E1 | 15.0 | 0.45067 | 0.450654 | 0.451023 | 0.012899 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-15 20:16:56 | 0.005 sec | 6 | .33E1 | 15.0 | 0.448406 | 0.448429 | 0.448858 | 0.012821 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-15 20:16:56 | 0.008 sec | 8 | .21E1 | 15.0 | 0.444887 | 0.444968 | 0.44549 | 0.012701 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-15 20:16:56 | 0.010 sec | 10 | .13E1 | 15.0 | 0.439585 | 0.439748 | 0.440403 | 0.012525 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-15 20:16:56 | 0.013 sec | 12 | .79E0 | 15.0 | 0.431944 | 0.432212 | 0.433043 | 0.012283 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-15 20:16:56 | 0.015 sec | 14 | .49E0 | 15.0 | 0.421769 | 0.422137 | 0.423177 | 0.011988 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-15 20:16:56 | 0.017 sec | 16 | .31E0 | 15.0 | 0.409871 | 0.410273 | 0.41153 | 0.011695 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-15 20:16:56 | 0.020 sec | 18 | .19E0 | 15.0 | 0.398221 | 0.398495 | 0.400003 | 0.011495 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-15 20:16:56 | 0.022 sec | 20 | .12E0 | 15.0 | 0.388804 | 0.38873 | 0.390635 | 0.011444 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-15 20:16:56 | 0.025 sec | 22 | .73E-1 | 15.0 | 0.382289 | 0.381678 | 0.384198 | 0.011523 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-15 20:16:57 | 0.028 sec | 24 | .45E-1 | 15.0 | 0.378213 | 0.376961 | 0.380273 | 0.011673 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-15 20:16:57 | 0.030 sec | 26 | .28E-1 | 15.0 | 0.375786 | 0.373881 | 0.378058 | 0.011843 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-15 20:16:57 | 0.033 sec | 28 | .18E-1 | 15.0 | 0.374357 | 0.371847 | 0.376384 | 0.011842 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-15 20:16:57 | 0.035 sec | 30 | .11E-1 | 15.0 | 0.373518 | 0.370484 | 0.375605 | 0.011918 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-15 20:16:57 | 0.038 sec | 32 | .68E-2 | 15.0 | 0.373032 | 0.369567 | 0.375187 | 0.011977 | 0.0 | NaN | |||||||||||||||
| 16 | 2021-07-15 20:16:56 | 0.304 sec | 33 | None | NaN | 33.0 | 0.219391 | 0.186379 | 0.148024 | 0.770793 | 0.289212 | 8.318376 | 0.078217 | 0.218688 | 0.184477 | 0.153269 | 0.790554 | 0.294321 | 9.984615 | 0.07396 | ||||||
| 17 | 2021-07-15 20:16:57 | 0.041 sec | 34 | .42E-2 | 15.0 | 0.372759 | 0.368955 | 0.374981 | 0.012023 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-15 20:16:57 | 0.043 sec | 35 | .26E-2 | 15.0 | 0.372613 | 0.368555 | 0.379871 | 0.012669 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-15 20:16:57 | 0.044 sec | 36 | .16E-2 | 15.0 | 0.372535 | 0.368286 | 0.384164 | 0.013635 | 0.0 | NaN |
See the whole table with table.as_data_frame() Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.568888 | 1.000000 | 0.259244 |
| 1 | Average_Transaction_Frequency | 0.280105 | 0.492373 | 0.127645 |
| 2 | Merchant_ID | 0.205506 | 0.361243 | 0.093650 |
| 3 | Minimum_Transaction_Amount | 0.188753 | 0.331792 | 0.086015 |
| 4 | Card_Type.1 | 0.178540 | 0.313841 | 0.081361 |
| 5 | Channel_ID | 0.178035 | 0.312953 | 0.081131 |
| 6 | Card_Type.0 | 0.174502 | 0.306743 | 0.079521 |
| 7 | Maximum_Transaction_Amount | 0.107826 | 0.189539 | 0.049137 |
| 8 | Transaction_Amount | 0.106561 | 0.187314 | 0.048560 |
| 9 | Transaction_Date | 0.082682 | 0.145340 | 0.037679 |
| 10 | Average_Transaction_Amount | 0.043530 | 0.076517 | 0.019837 |
| 11 | Day | 0.032815 | 0.057682 | 0.014954 |
| 12 | Month | 0.027390 | 0.048146 | 0.012482 |
| 13 | City_ID | 0.019277 | 0.033885 | 0.008784 |
totalF1 = []
for i in models:
F1s=i.F1(valid=True)[0][1]
totalF1.append(F1s)
a=statistics.mean(totalF1)
a
0.42137432219041765
totalthreshold = []
for i in models:
thresholds=i.F1(valid=True)[0][0]
totalthreshold.append(thresholds)
b=statistics.mean(totalthreshold)
b
0.24674137793754738
totalprecision = []
for i in models:
precisions=i.precision(valid=True)[0][1]
totalprecision.append(precisions)
c=statistics.mean(totalprecision)
c
0.9309923932750019
totalrecall = []
for i in models:
recalls=i.recall(valid=True)[0][1]
totalrecall.append(recalls)
d=statistics.mean(totalrecall)
d
Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0. Could not find exact threshold 0.0; using closest threshold found 0.0.
1.0
totalaccuracy = []
for i in models:
accuracies=i.accuracy(valid=True)[0][1]
totalaccuracy.append(accuracies)
e=statistics.mean(totalaccuracy)
e
0.9459681561376476
f1 = a
threshold = b
precision = c
sensitivity = d
accuracy = e
metrics = {'Threshold': threshold, 'F1': f1, 'Precision': precision, 'Sensitivity': sensitivity, 'Accuracy': accuracy}
gbm_performance = pd.DataFrame(metrics.values(), columns=['Value'], index=metrics.keys())
gbm_performance
| Value | |
|---|---|
| Threshold | 0.246741 |
| F1 | 0.421374 |
| Precision | 0.930992 |
| Sensitivity | 1.000000 |
| Accuracy | 0.945968 |
gbm.varimp_plot()
start_time = pd.to_datetime('now') + pd.Timedelta('07:00:00')
models=[]
for train, test in zip(trains, tests):
top_3vars = ['Card_Holder', 'Average_Transaction_Frequency', 'Merchant_ID']
GLM = H2OAutoML(max_runtime_secs=10000, nfolds=30, include_algos=['GLM'])
GLM.train(x=glm_vars, y='Fraud_Status', training_frame=train, validation_frame=test)
models.append(GLM.leader)
timer(start_time, header='\nModeling')
--------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) /Applications/anaconda3/lib/python3.8/site-packages/urllib3/connection.py in _new_conn(self) 168 try: --> 169 conn = connection.create_connection( 170 (self._dns_host, self.port), self.timeout, **extra_kw /Applications/anaconda3/lib/python3.8/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 95 if err is not None: ---> 96 raise err 97 /Applications/anaconda3/lib/python3.8/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 85 sock.bind(source_address) ---> 86 sock.connect(sa) 87 return sock ConnectionRefusedError: [Errno 61] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) /Applications/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 698 # Make the request on the httplib connection object. --> 699 httplib_response = self._make_request( 700 conn, /Applications/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 393 else: --> 394 conn.request(method, url, **httplib_request_kw) 395 /Applications/anaconda3/lib/python3.8/site-packages/urllib3/connection.py in request(self, method, url, body, headers) 233 headers["User-Agent"] = _get_default_user_agent() --> 234 super(HTTPConnection, self).request(method, url, body=body, headers=headers) 235 /Applications/anaconda3/lib/python3.8/http/client.py in request(self, method, url, body, headers, encode_chunked) 1254 """Send a complete request to the server.""" -> 1255 self._send_request(method, url, body, headers, encode_chunked) 1256 /Applications/anaconda3/lib/python3.8/http/client.py in _send_request(self, method, url, body, headers, encode_chunked) 1300 body = _encode(body, 'body') -> 1301 self.endheaders(body, encode_chunked=encode_chunked) 1302 /Applications/anaconda3/lib/python3.8/http/client.py in endheaders(self, message_body, encode_chunked) 1249 raise CannotSendHeader() -> 1250 self._send_output(message_body, encode_chunked=encode_chunked) 1251 /Applications/anaconda3/lib/python3.8/http/client.py in _send_output(self, message_body, encode_chunked) 1009 del self._buffer[:] -> 1010 self.send(msg) 1011 /Applications/anaconda3/lib/python3.8/http/client.py in send(self, data) 949 if self.auto_open: --> 950 self.connect() 951 else: /Applications/anaconda3/lib/python3.8/site-packages/urllib3/connection.py in connect(self) 199 def connect(self): --> 200 conn = self._new_conn() 201 self._prepare_conn(conn) /Applications/anaconda3/lib/python3.8/site-packages/urllib3/connection.py in _new_conn(self) 180 except SocketError as e: --> 181 raise NewConnectionError( 182 self, "Failed to establish a new connection: %s" % e NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7fc66fde3760>: Failed to establish a new connection: [Errno 61] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) /Applications/anaconda3/lib/python3.8/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 438 if not chunked: --> 439 resp = conn.urlopen( 440 method=request.method, /Applications/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 754 --> 755 retries = retries.increment( 756 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] /Applications/anaconda3/lib/python3.8/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 573 if new_retry.is_exhausted(): --> 574 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 575 MaxRetryError: HTTPConnectionPool(host='localhost', port=54321): Max retries exceeded with url: /3/Metadata/schemas/AutoMLV99 (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fc66fde3760>: Failed to establish a new connection: [Errno 61] Connection refused')) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) /Applications/anaconda3/lib/python3.8/site-packages/h2o/backend/connection.py in request(self, endpoint, data, json, filename, save_to) 471 verify = self._cacert if self._verify_ssl_cert and self._cacert else self._verify_ssl_cert --> 472 resp = requests.request(method=method, url=url, data=data, json=json, files=files, params=params, 473 headers=headers, timeout=self._timeout, stream=stream, /Applications/anaconda3/lib/python3.8/site-packages/requests/api.py in request(method, url, **kwargs) 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 /Applications/anaconda3/lib/python3.8/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 541 send_kwargs.update(settings) --> 542 resp = self.send(prep, **send_kwargs) 543 /Applications/anaconda3/lib/python3.8/site-packages/requests/sessions.py in send(self, request, **kwargs) 654 # Send the request --> 655 r = adapter.send(request, **kwargs) 656 /Applications/anaconda3/lib/python3.8/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 515 --> 516 raise ConnectionError(e, request=request) 517 ConnectionError: HTTPConnectionPool(host='localhost', port=54321): Max retries exceeded with url: /3/Metadata/schemas/AutoMLV99 (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fc66fde3760>: Failed to establish a new connection: [Errno 61] Connection refused')) During handling of the above exception, another exception occurred: H2OConnectionError Traceback (most recent call last) <ipython-input-197-61ebd3ca6477> in <module> 3 for train, test in zip(trains, tests): 4 top_3vars = ['Card_Holder', 'Average_Transaction_Frequency', 'Merchant_ID'] ----> 5 GLM = H2OAutoML(max_runtime_secs=10000, nfolds=30, include_algos=['GLM']) 6 GLM.train(x=glm_vars, y='Fraud_Status', training_frame=train, validation_frame=test) 7 models.append(GLM.leader) /Applications/anaconda3/lib/python3.8/site-packages/h2o/automl/autoh2o.py in __init__(self, nfolds, balance_classes, class_sampling_factors, max_after_balance_size, max_runtime_secs, max_runtime_secs_per_model, max_models, stopping_metric, stopping_tolerance, stopping_rounds, seed, project_name, exclude_algos, include_algos, exploitation_ratio, modeling_plan, preprocessing, monotone_constraints, keep_cross_validation_predictions, keep_cross_validation_models, keep_cross_validation_fold_assignment, sort_metric, export_checkpoints_dir, verbosity, **kwargs) 153 # Check if H2O jar contains AutoML 154 try: --> 155 h2o.api("GET /3/Metadata/schemas/AutoMLV99") 156 except h2o.exceptions.H2OResponseError as e: 157 print(e) /Applications/anaconda3/lib/python3.8/site-packages/h2o/h2o.py in api(endpoint, data, json, filename, save_to) 105 # type checks are performed in H2OConnection class 106 _check_connection() --> 107 return h2oconn.request(endpoint, data=data, json=json, filename=filename, save_to=save_to) 108 109 /Applications/anaconda3/lib/python3.8/site-packages/h2o/backend/connection.py in request(self, endpoint, data, json, filename, save_to) 484 else: 485 self._log_end_exception(e) --> 486 raise H2OConnectionError("Unexpected HTTP error: %s" % e) 487 except requests.exceptions.Timeout as e: 488 self._log_end_exception(e) H2OConnectionError: Unexpected HTTP error: HTTPConnectionPool(host='localhost', port=54321): Max retries exceeded with url: /3/Metadata/schemas/AutoMLV99 (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fc66fde3760>: Failed to establish a new connection: [Errno 61] Connection refused'))
glm_3vars
Model Details ============= H2OGeneralizedLinearEstimator : Generalized Linear Modeling Model Key: GLM_1_AutoML_20210701_182845 GLM Model: summary
| family | link | regularization | lambda_search | number_of_predictors_total | number_of_active_predictors | number_of_iterations | training_frame | ||
|---|---|---|---|---|---|---|---|---|---|
| 0 | binomial | logit | Ridge ( lambda = 0.002542 ) | nlambda = 30, lambda.max = 8.3628, lambda.min = 0.002542, lambda.1... | 3 | 3 | 35 | automl_training_py_830_sid_9d88 |
ModelMetricsBinomialGLM: glm ** Reported on train data. ** MSE: 0.04909037783131847 RMSE: 0.2215634848780784 LogLoss: 0.19104180709294746 Null degrees of freedom: 7785 Residual degrees of freedom: 7782 Null deviance: 3538.959233793834 Residual deviance: 2974.9030200513785 AIC: 2982.9030200513785 AUC: 0.7718163035531168 AUCPR: 0.2793035805825629 Gini: 0.5436326071062336 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.25003107330309315:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7013.0 | 305.0 | 0.0417 | (305.0/7318.0) |
| 1 | 1 | 278.0 | 190.0 | 0.594 | (278.0/468.0) |
| 2 | Total | 7291.0 | 495.0 | 0.0749 | (583.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.250031 | 0.394600 | 151.0 |
| 1 | max f2 | 0.047110 | 0.408027 | 236.0 |
| 2 | max f0point5 | 0.399199 | 0.396679 | 50.0 |
| 3 | max accuracy | 0.405233 | 0.942846 | 44.0 |
| 4 | max precision | 0.509934 | 0.700000 | 6.0 |
| 5 | max recall | 0.009290 | 1.000000 | 393.0 |
| 6 | max specificity | 0.589423 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.250031 | 0.354885 | 151.0 |
| 8 | max min_per_class_accuracy | 0.042535 | 0.711538 | 260.0 |
| 9 | max mean_per_class_accuracy | 0.042274 | 0.715250 | 262.0 |
| 10 | max tns | 0.589423 | 7317.000000 | 0.0 |
| 11 | max fns | 0.589423 | 468.000000 | 0.0 |
| 12 | max fps | 0.000505 | 7318.000000 | 399.0 |
| 13 | max tps | 0.009290 | 468.000000 | 393.0 |
| 14 | max tnr | 0.589423 | 0.999863 | 0.0 |
| 15 | max fnr | 0.589423 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000505 | 1.000000 | 399.0 |
| 17 | max tpr | 0.009290 | 1.000000 | 393.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.414163 | 8.318376 | 8.318376 | 0.500000 | 0.448625 | 0.500000 | 0.448625 | 0.083333 | 0.083333 | 731.837607 | 731.837607 | 0.078004 |
| 1 | 2 | 0.020036 | 0.398419 | 10.024710 | 9.171543 | 0.602564 | 0.406466 | 0.551282 | 0.427546 | 0.100427 | 0.183761 | 902.470962 | 817.154284 | 0.174195 |
| 2 | 3 | 0.030182 | 0.386008 | 4.633020 | 7.645827 | 0.278481 | 0.392402 | 0.459574 | 0.415731 | 0.047009 | 0.230769 | 363.301958 | 664.582651 | 0.213415 |
| 3 | 4 | 0.040072 | 0.367149 | 3.673049 | 6.665365 | 0.220779 | 0.377759 | 0.400641 | 0.406360 | 0.036325 | 0.267094 | 267.304917 | 566.536544 | 0.241541 |
| 4 | 5 | 0.050090 | 0.341999 | 5.119001 | 6.356092 | 0.307692 | 0.356021 | 0.382051 | 0.396292 | 0.051282 | 0.318376 | 411.900066 | 535.609248 | 0.285444 |
| 5 | 6 | 0.100180 | 0.060080 | 2.516842 | 4.436467 | 0.151282 | 0.143221 | 0.266667 | 0.269757 | 0.126068 | 0.444444 | 151.684199 | 343.646724 | 0.366281 |
| 6 | 7 | 0.150141 | 0.046701 | 1.710720 | 3.529439 | 0.102828 | 0.049786 | 0.212147 | 0.196558 | 0.085470 | 0.529915 | 71.072001 | 252.943929 | 0.404060 |
| 7 | 8 | 0.200488 | 0.044860 | 1.145899 | 2.930882 | 0.068878 | 0.045708 | 0.176169 | 0.158677 | 0.057692 | 0.587607 | 14.589874 | 193.088202 | 0.411876 |
| 8 | 9 | 0.300026 | 0.042698 | 1.030405 | 2.300373 | 0.061935 | 0.043757 | 0.138271 | 0.120551 | 0.102564 | 0.690171 | 3.040529 | 130.037283 | 0.415096 |
| 9 | 10 | 0.400077 | 0.041029 | 0.832905 | 1.933388 | 0.050064 | 0.041860 | 0.116212 | 0.100872 | 0.083333 | 0.773504 | -16.709457 | 93.338821 | 0.397309 |
| 10 | 11 | 0.500000 | 0.039367 | 0.384912 | 1.623932 | 0.023136 | 0.040213 | 0.097611 | 0.088749 | 0.038462 | 0.811966 | -61.508800 | 62.393162 | 0.331917 |
| 11 | 12 | 0.600051 | 0.037299 | 0.405774 | 1.420819 | 0.024390 | 0.038423 | 0.085402 | 0.080358 | 0.040598 | 0.852564 | -59.422556 | 42.081852 | 0.268661 |
| 12 | 13 | 0.699974 | 0.035172 | 0.470448 | 1.285151 | 0.028278 | 0.036206 | 0.077248 | 0.074055 | 0.047009 | 0.899573 | -52.955200 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.031902 | 0.298992 | 1.161822 | 0.017972 | 0.033664 | 0.069835 | 0.069004 | 0.029915 | 0.929487 | -70.100831 | 16.182167 | 0.137741 |
| 14 | 15 | 0.899949 | 0.026620 | 0.384912 | 1.075560 | 0.023136 | 0.029489 | 0.064650 | 0.064616 | 0.038462 | 0.967949 | -61.508800 | 7.555997 | 0.072349 |
| 15 | 16 | 1.000000 | 0.000431 | 0.320348 | 1.000000 | 0.019255 | 0.019606 | 0.060108 | 0.060113 | 0.032051 | 1.000000 | -67.965176 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on validation data. ** MSE: 0.04715765970836517 RMSE: 0.2171581444670339 LogLoss: 0.18324846751149748 Null degrees of freedom: 1946 Residual degrees of freedom: 1943 Null deviance: 884.801798631153 Residual deviance: 713.5695324897711 AIC: 721.5695324897711 AUC: 0.7501144271636074 AUCPR: 0.30288900816332487 Gini: 0.5002288543272149 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.08655307529027566:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 1743.0 | 87.0 | 0.0475 | (87.0/1830.0) |
| 1 | 1 | 58.0 | 59.0 | 0.4957 | (58.0/117.0) |
| 2 | Total | 1801.0 | 146.0 | 0.0745 | (145.0/1947.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.086553 | 0.448669 | 124.0 |
| 1 | max f2 | 0.086553 | 0.480456 | 124.0 |
| 2 | max f0point5 | 0.260940 | 0.434439 | 107.0 |
| 3 | max accuracy | 0.415247 | 0.944016 | 22.0 |
| 4 | max precision | 0.421747 | 0.684211 | 18.0 |
| 5 | max recall | 0.021181 | 1.000000 | 361.0 |
| 6 | max specificity | 0.633405 | 0.999454 | 0.0 |
| 7 | max absolute_mcc | 0.086553 | 0.412142 | 124.0 |
| 8 | max min_per_class_accuracy | 0.041989 | 0.666667 | 223.0 |
| 9 | max mean_per_class_accuracy | 0.086553 | 0.728366 | 124.0 |
| 10 | max tns | 0.633405 | 1829.000000 | 0.0 |
| 11 | max fns | 0.633405 | 117.000000 | 0.0 |
| 12 | max fps | 0.000446 | 1830.000000 | 399.0 |
| 13 | max tps | 0.021181 | 117.000000 | 361.0 |
| 14 | max tnr | 0.633405 | 0.999454 | 0.0 |
| 15 | max fnr | 0.633405 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000446 | 1.000000 | 399.0 |
| 17 | max tpr | 0.021181 | 1.000000 | 361.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.14 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010272 | 0.420353 | 11.648718 | 11.648718 | 0.700000 | 0.458553 | 0.700000 | 0.458553 | 0.119658 | 0.119658 | 1064.871795 | 1064.871795 | 0.116379 |
| 1 | 2 | 0.020031 | 0.401879 | 6.130904 | 8.960552 | 0.368421 | 0.411187 | 0.538462 | 0.435477 | 0.059829 | 0.179487 | 513.090418 | 796.055227 | 0.169651 |
| 2 | 3 | 0.030303 | 0.384610 | 6.656410 | 8.179487 | 0.400000 | 0.393471 | 0.491525 | 0.421238 | 0.068376 | 0.247863 | 565.641026 | 717.948718 | 0.231470 |
| 3 | 4 | 0.040062 | 0.365290 | 5.255061 | 7.467127 | 0.315789 | 0.376647 | 0.448718 | 0.410376 | 0.051282 | 0.299145 | 425.506073 | 646.712689 | 0.275648 |
| 4 | 5 | 0.050334 | 0.351965 | 3.328205 | 6.622449 | 0.200000 | 0.359967 | 0.397959 | 0.400088 | 0.034188 | 0.333333 | 232.820513 | 562.244898 | 0.301093 |
| 5 | 6 | 0.100154 | 0.060250 | 3.431139 | 5.034977 | 0.206186 | 0.165961 | 0.302564 | 0.283625 | 0.170940 | 0.504274 | 243.113931 | 403.497699 | 0.429957 |
| 6 | 7 | 0.149974 | 0.046661 | 0.514671 | 3.533368 | 0.030928 | 0.049849 | 0.212329 | 0.205967 | 0.025641 | 0.529915 | -48.532910 | 253.336846 | 0.404231 |
| 7 | 8 | 0.200308 | 0.044852 | 0.849032 | 2.858843 | 0.051020 | 0.045714 | 0.171795 | 0.165698 | 0.042735 | 0.572650 | -15.096808 | 185.884287 | 0.396147 |
| 8 | 9 | 0.299949 | 0.042749 | 0.686228 | 2.137118 | 0.041237 | 0.043712 | 0.128425 | 0.125175 | 0.068376 | 0.641026 | -31.377214 | 113.711802 | 0.362884 |
| 9 | 10 | 0.400616 | 0.040992 | 0.679226 | 1.770776 | 0.040816 | 0.041842 | 0.106410 | 0.104235 | 0.068376 | 0.709402 | -32.077446 | 77.077581 | 0.328527 |
| 10 | 11 | 0.500257 | 0.039326 | 0.600449 | 1.537672 | 0.036082 | 0.040195 | 0.092402 | 0.091479 | 0.059829 | 0.769231 | -39.955062 | 53.767177 | 0.286171 |
| 11 | 12 | 0.599897 | 0.037248 | 0.600449 | 1.382003 | 0.036082 | 0.038343 | 0.083048 | 0.082654 | 0.059829 | 0.829060 | -39.955062 | 38.200299 | 0.243814 |
| 12 | 13 | 0.700051 | 0.034827 | 0.426693 | 1.245330 | 0.025641 | 0.036011 | 0.074835 | 0.075981 | 0.042735 | 0.871795 | -57.330703 | 24.532987 | 0.182724 |
| 13 | 14 | 0.799692 | 0.032119 | 0.343114 | 1.132915 | 0.020619 | 0.033491 | 0.068080 | 0.070686 | 0.034188 | 0.905983 | -65.688607 | 13.291504 | 0.113087 |
| 14 | 15 | 0.899846 | 0.026516 | 0.597370 | 1.073308 | 0.035897 | 0.029738 | 0.064498 | 0.066129 | 0.059829 | 0.965812 | -40.262985 | 7.330816 | 0.070184 |
| 15 | 16 | 1.000000 | 0.000440 | 0.341354 | 1.000000 | 0.020513 | 0.018527 | 0.060092 | 0.061361 | 0.034188 | 1.000000 | -65.864563 | 0.000000 | 0.000000 |
ModelMetricsBinomialGLM: glm ** Reported on cross-validation data. ** MSE: 0.04918396499714938 RMSE: 0.2217745814947001 LogLoss: 0.19150429178700995 Null degrees of freedom: 7785 Residual degrees of freedom: 7782 Null deviance: 3540.9687371308805 Residual deviance: 2982.104831707319 AIC: 2990.104831707319 AUC: 0.766707865864056 AUCPR: 0.2700517998881614 Gini: 0.5334157317281121 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.24807023134781647:
| 0 | 1 | Error | Rate | ||
|---|---|---|---|---|---|
| 0 | 0 | 7010.0 | 308.0 | 0.0421 | (308.0/7318.0) |
| 1 | 1 | 278.0 | 190.0 | 0.594 | (278.0/468.0) |
| 2 | Total | 7288.0 | 498.0 | 0.0753 | (586.0/7786.0) |
Maximum Metrics: Maximum metrics at their respective thresholds
| metric | threshold | value | idx | |
|---|---|---|---|---|
| 0 | max f1 | 0.248070 | 0.393375 | 152.0 |
| 1 | max f2 | 0.047509 | 0.405042 | 238.0 |
| 2 | max f0point5 | 0.319978 | 0.393309 | 119.0 |
| 3 | max accuracy | 0.410154 | 0.941176 | 39.0 |
| 4 | max precision | 0.503592 | 0.636364 | 8.0 |
| 5 | max recall | 0.004185 | 1.000000 | 398.0 |
| 6 | max specificity | 0.686384 | 0.999863 | 0.0 |
| 7 | max absolute_mcc | 0.248070 | 0.353489 | 152.0 |
| 8 | max min_per_class_accuracy | 0.042400 | 0.696581 | 265.0 |
| 9 | max mean_per_class_accuracy | 0.044067 | 0.710330 | 255.0 |
| 10 | max tns | 0.686384 | 7317.000000 | 0.0 |
| 11 | max fns | 0.686384 | 468.000000 | 0.0 |
| 12 | max fps | 0.000623 | 7318.000000 | 399.0 |
| 13 | max tps | 0.004185 | 468.000000 | 398.0 |
| 14 | max tnr | 0.686384 | 0.999863 | 0.0 |
| 15 | max fnr | 0.686384 | 1.000000 | 0.0 |
| 16 | max fpr | 0.000623 | 1.000000 | 399.0 |
| 17 | max tpr | 0.004185 | 1.000000 | 398.0 |
Gains/Lift Table: Avg response rate: 6.01 %, avg score: 6.01 %
| group | cumulative_data_fraction | lower_threshold | lift | cumulative_lift | response_rate | score | cumulative_response_rate | cumulative_score | capture_rate | cumulative_capture_rate | gain | cumulative_gain | kolmogorov_smirnov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.010018 | 0.415186 | 8.744959 | 8.744959 | 0.525641 | 0.449035 | 0.525641 | 0.449035 | 0.087607 | 0.087607 | 774.495946 | 774.495946 | 0.082551 |
| 1 | 2 | 0.020036 | 0.397325 | 8.318376 | 8.531668 | 0.500000 | 0.405796 | 0.512821 | 0.427415 | 0.083333 | 0.170940 | 731.837607 | 753.166776 | 0.160555 |
| 2 | 3 | 0.030054 | 0.384387 | 5.332292 | 7.465209 | 0.320513 | 0.390195 | 0.448718 | 0.415009 | 0.053419 | 0.224359 | 433.229235 | 646.520929 | 0.206731 |
| 3 | 4 | 0.040072 | 0.367516 | 4.265834 | 6.665365 | 0.256410 | 0.377164 | 0.400641 | 0.405547 | 0.042735 | 0.267094 | 326.583388 | 566.536544 | 0.241541 |
| 4 | 5 | 0.050090 | 0.342361 | 5.119001 | 6.356092 | 0.307692 | 0.355640 | 0.382051 | 0.395566 | 0.051282 | 0.318376 | 411.900066 | 535.609248 | 0.285444 |
| 5 | 6 | 0.100308 | 0.057656 | 2.467856 | 4.409485 | 0.148338 | 0.142581 | 0.265045 | 0.268912 | 0.123932 | 0.442308 | 146.785582 | 340.948488 | 0.363871 |
| 6 | 7 | 0.150013 | 0.046960 | 1.676572 | 3.503973 | 0.100775 | 0.049799 | 0.210616 | 0.196312 | 0.083333 | 0.525641 | 67.657192 | 250.397348 | 0.399650 |
| 7 | 8 | 0.200103 | 0.045081 | 1.194433 | 2.925847 | 0.071795 | 0.045873 | 0.175866 | 0.158654 | 0.059829 | 0.585470 | 19.443349 | 192.584729 | 0.410012 |
| 8 | 9 | 0.300026 | 0.042730 | 0.983664 | 2.279007 | 0.059126 | 0.043853 | 0.136986 | 0.120420 | 0.098291 | 0.683761 | -1.633599 | 127.900714 | 0.408276 |
| 9 | 10 | 0.400077 | 0.041003 | 0.832905 | 1.917366 | 0.050064 | 0.041816 | 0.115249 | 0.100762 | 0.083333 | 0.767094 | -16.709457 | 91.736566 | 0.390488 |
| 10 | 11 | 0.500000 | 0.039358 | 0.427680 | 1.619658 | 0.025707 | 0.040197 | 0.097354 | 0.088659 | 0.042735 | 0.809829 | -57.232000 | 61.965812 | 0.329643 |
| 11 | 12 | 0.600051 | 0.037373 | 0.427131 | 1.420819 | 0.025674 | 0.038448 | 0.085402 | 0.080287 | 0.042735 | 0.852564 | -57.286901 | 42.081852 | 0.268661 |
| 12 | 13 | 0.699974 | 0.035147 | 0.470448 | 1.285151 | 0.028278 | 0.036281 | 0.077248 | 0.074005 | 0.047009 | 0.899573 | -52.955200 | 28.515094 | 0.212363 |
| 13 | 14 | 0.800026 | 0.031965 | 0.298992 | 1.161822 | 0.017972 | 0.033718 | 0.069835 | 0.068966 | 0.029915 | 0.929487 | -70.100831 | 16.182167 | 0.137741 |
| 14 | 15 | 0.899949 | 0.026769 | 0.342144 | 1.070811 | 0.020566 | 0.029614 | 0.064364 | 0.064597 | 0.034188 | 0.963675 | -65.785600 | 7.081136 | 0.067802 |
| 15 | 16 | 1.000000 | 0.000339 | 0.363061 | 1.000000 | 0.021823 | 0.019712 | 0.060108 | 0.060106 | 0.036325 | 1.000000 | -63.693866 | 0.000000 | 0.000000 |
Cross-Validation Metrics Summary:
| mean | sd | cv_1_valid | cv_2_valid | cv_3_valid | cv_4_valid | cv_5_valid | cv_6_valid | cv_7_valid | cv_8_valid | cv_9_valid | cv_10_valid | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | accuracy | 0.92782176 | 0.010034885 | 0.915276 | 0.93196404 | 0.92426187 | 0.93324775 | 0.9114249 | 0.93068033 | 0.933162 | 0.91902316 | 0.9434447 | 0.93573266 |
| 1 | auc | 0.77223945 | 0.045481242 | 0.7394508 | 0.8261303 | 0.80780524 | 0.75610393 | 0.7325666 | 0.70058906 | 0.76954305 | 0.7751511 | 0.8516768 | 0.7633773 |
| 2 | err | 0.07217826 | 0.010034885 | 0.084724 | 0.068035945 | 0.075738125 | 0.06675225 | 0.088575095 | 0.06931964 | 0.06683805 | 0.080976866 | 0.05655527 | 0.06426735 |
| 3 | err_count | 56.2 | 7.828722 | 66.0 | 53.0 | 59.0 | 52.0 | 69.0 | 54.0 | 52.0 | 63.0 | 44.0 | 50.0 |
| 4 | f0point5 | 0.40584818 | 0.067252986 | 0.3888889 | 0.50751877 | 0.40650406 | 0.39106146 | 0.29411766 | 0.34246576 | 0.3909465 | 0.37401575 | 0.46296296 | 0.5 |
| 5 | f1 | 0.40413105 | 0.055801786 | 0.3888889 | 0.5046729 | 0.4040404 | 0.35 | 0.33009708 | 0.35714287 | 0.42222223 | 0.37623763 | 0.47619048 | 0.4318182 |
| 6 | f2 | 0.4065956 | 0.05846419 | 0.3888889 | 0.5018587 | 0.40160644 | 0.3167421 | 0.3761062 | 0.37313432 | 0.4589372 | 0.37848607 | 0.49019608 | 0.38 |
| 7 | lift_top_group | 9.482831 | 2.2407374 | 10.819445 | 7.212963 | 5.8425 | 8.287234 | 14.25 | 9.98718 | 9.974359 | 9.725 | 9.725 | 9.00463 |
| 8 | logloss | 0.1910588 | 0.020382663 | 0.21951564 | 0.19521089 | 0.19931845 | 0.20096655 | 0.18304121 | 0.17297754 | 0.16346253 | 0.20789565 | 0.15947707 | 0.20872252 |
| 9 | max_per_class_error | 0.5894958 | 0.06684932 | 0.6111111 | 0.5 | 0.6 | 0.70212764 | 0.58536583 | 0.61538464 | 0.51282054 | 0.62 | 0.5 | 0.6481481 |
| 10 | mcc | 0.36929342 | 0.05838778 | 0.34337166 | 0.46817335 | 0.3636273 | 0.32144964 | 0.2917755 | 0.3216228 | 0.3913502 | 0.33296186 | 0.44693723 | 0.4116646 |
| 11 | mean_per_class_accuracy | 0.68579566 | 0.032414325 | 0.6716858 | 0.73206896 | 0.68010974 | 0.635958 | 0.6768293 | 0.6720374 | 0.72193885 | 0.668022 | 0.73373985 | 0.6655668 |
| 12 | mean_per_class_error | 0.31420434 | 0.032414325 | 0.3283142 | 0.26793104 | 0.31989026 | 0.36404198 | 0.32317072 | 0.32796258 | 0.27806112 | 0.33197802 | 0.26626018 | 0.3344332 |
| 13 | mse | 0.049124226 | 0.006072581 | 0.057325825 | 0.051920164 | 0.052350078 | 0.05107389 | 0.046125334 | 0.04280962 | 0.041166365 | 0.053872194 | 0.040186133 | 0.05441266 |
| 14 | null_deviance | 354.09686 | 35.606644 | 393.817 | 393.817 | 371.5971 | 355.04553 | 322.22958 | 311.375 | 311.24603 | 371.47516 | 316.66873 | 393.69763 |
| 15 | pr_auc | 0.28898233 | 0.04584106 | 0.32032335 | 0.35812753 | 0.27804968 | 0.26166928 | 0.24724641 | 0.23134747 | 0.2530715 | 0.26707545 | 0.3147415 | 0.35817108 |
| 16 | precision | 0.40967223 | 0.08286334 | 0.3888889 | 0.509434 | 0.40816328 | 0.42424244 | 0.27419356 | 0.33333334 | 0.37254903 | 0.37254903 | 0.45454547 | 0.5588235 |
| 17 | r2 | 0.12822026 | 0.03806318 | 0.111426294 | 0.19521622 | 0.1284452 | 0.09912421 | 0.074930795 | 0.099839464 | 0.1354449 | 0.104176424 | 0.17601545 | 0.1575836 |
| 18 | recall | 0.41050422 | 0.06684932 | 0.3888889 | 0.5 | 0.4 | 0.29787233 | 0.41463414 | 0.3846154 | 0.4871795 | 0.38 | 0.5 | 0.35185185 |
| 19 | residual_deviance | 297.5217 | 31.782501 | 342.00537 | 304.13855 | 310.53815 | 313.1059 | 285.17822 | 269.499 | 254.3477 | 323.48563 | 248.14632 | 324.77222 |
See the whole table with table.as_data_frame() Scoring History:
| timestamp | duration | iteration | lambda | predictors | deviance_train | deviance_test | deviance_xval | deviance_se | alpha | iterations | training_rmse | training_logloss | training_r2 | training_auc | training_pr_auc | training_lift | training_classification_error | validation_rmse | validation_logloss | validation_r2 | validation_auc | validation_pr_auc | validation_lift | validation_classification_error | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2021-07-01 18:28:47 | 0.000 sec | 2 | .84E1 | 4 | 0.452814 | 0.452448 | 0.453245 | 0.014397 | 0.0 | NaN | |||||||||||||||
| 1 | 2021-07-01 18:28:47 | 0.004 sec | 4 | .52E1 | 4 | 0.451785 | 0.451250 | 0.452321 | 0.014365 | 0.0 | NaN | |||||||||||||||
| 2 | 2021-07-01 18:28:47 | 0.006 sec | 6 | .32E1 | 4 | 0.450156 | 0.449351 | 0.450855 | 0.014316 | 0.0 | NaN | |||||||||||||||
| 3 | 2021-07-01 18:28:47 | 0.008 sec | 8 | .2E1 | 4 | 0.447605 | 0.446375 | 0.448551 | 0.014239 | 0.0 | NaN | |||||||||||||||
| 4 | 2021-07-01 18:28:47 | 0.010 sec | 10 | .12E1 | 4 | 0.443691 | 0.441802 | 0.444994 | 0.014121 | 0.0 | NaN | |||||||||||||||
| 5 | 2021-07-01 18:28:47 | 0.011 sec | 12 | .77E0 | 4 | 0.437877 | 0.434986 | 0.439672 | 0.013949 | 0.0 | NaN | |||||||||||||||
| 6 | 2021-07-01 18:28:47 | 0.013 sec | 14 | .48E0 | 4 | 0.429754 | 0.425413 | 0.432105 | 0.013714 | 0.0 | NaN | |||||||||||||||
| 7 | 2021-07-01 18:28:47 | 0.015 sec | 16 | .3E0 | 4 | 0.419506 | 0.413224 | 0.422292 | 0.013427 | 0.0 | NaN | |||||||||||||||
| 8 | 2021-07-01 18:28:47 | 0.017 sec | 18 | .19E0 | 4 | 0.408497 | 0.399912 | 0.411319 | 0.013138 | 0.0 | NaN | |||||||||||||||
| 9 | 2021-07-01 18:28:47 | 0.019 sec | 20 | .11E0 | 4 | 0.398807 | 0.387876 | 0.401214 | 0.012922 | 0.0 | NaN | |||||||||||||||
| 10 | 2021-07-01 18:28:47 | 0.020 sec | 22 | .71E-1 | 4 | 0.391771 | 0.378817 | 0.393589 | 0.012815 | 0.0 | NaN | |||||||||||||||
| 11 | 2021-07-01 18:28:47 | 0.022 sec | 24 | .44E-1 | 4 | 0.387348 | 0.372920 | 0.388689 | 0.012799 | 0.0 | NaN | |||||||||||||||
| 12 | 2021-07-01 18:28:47 | 0.024 sec | 26 | .28E-1 | 4 | 0.384810 | 0.369493 | 0.385861 | 0.012833 | 0.0 | NaN | |||||||||||||||
| 13 | 2021-07-01 18:28:47 | 0.027 sec | 28 | .17E-1 | 4 | 0.383429 | 0.367697 | 0.384342 | 0.012887 | 0.0 | NaN | |||||||||||||||
| 14 | 2021-07-01 18:28:47 | 0.029 sec | 30 | .11E-1 | 4 | 0.382704 | 0.366863 | 0.383572 | 0.012946 | 0.0 | NaN | |||||||||||||||
| 15 | 2021-07-01 18:28:47 | 0.032 sec | 32 | .66E-2 | 4 | 0.382338 | 0.366545 | 0.383208 | 0.013000 | 0.0 | 32.0 | 0.221563 | 0.191042 | 0.131066 | 0.771816 | 0.279304 | 8.318376 | 0.074878 | 0.217158 | 0.183248 | 0.165076 | 0.750114 | 0.302889 | 11.648718 | 0.074474 | |
| 16 | 2021-07-01 18:28:47 | 0.034 sec | 34 | .41E-2 | 4 | 0.382163 | 0.366475 | 0.383056 | 0.013046 | 0.0 | NaN | |||||||||||||||
| 17 | 2021-07-01 18:28:47 | 0.035 sec | 35 | .25E-2 | 4 | 0.382084 | 0.366497 | 0.383003 | 0.013081 | 0.0 | NaN | |||||||||||||||
| 18 | 2021-07-01 18:28:47 | 0.036 sec | 36 | .16E-2 | 4 | 0.382049 | 0.366550 | 0.388589 | 0.015844 | 0.0 | NaN | |||||||||||||||
| 19 | 2021-07-01 18:28:47 | 0.037 sec | 37 | .98E-3 | 4 | 0.382034 | 0.366598 | 0.388542 | 0.015848 | 0.0 | NaN |
See the whole table with table.as_data_frame() Variable Importances:
| variable | relative_importance | scaled_importance | percentage | |
|---|---|---|---|---|
| 0 | Card_Holder | 0.673323 | 1.000000 | 0.568817 |
| 1 | Average_Transaction_Frequency | 0.364135 | 0.540803 | 0.307618 |
| 2 | Transaction_Amount | 0.146268 | 0.217233 | 0.123566 |
totalF1 = []
for i in models:
F1s=i.F1(valid=True)[0][1]
totalF1.append(F1s)
a=statistics.mean(totalF1)
a
totalthreshold = []
for i in models:
thresholds=i.F1(valid=True)[0][0]
totalthreshold.append(thresholds)
b=statistics.mean(totalthreshold)
b
totalprecision = []
for i in models:
precisions=i.precision(valid=True)[0][1]
totalprecision.append(precisions)
c=statistics.mean(totalprecision)
c
totalrecall = []
for i in models:
recalls=i.recall(valid=True)[0][1]
totalrecall.append(recalls)
d=statistics.mean(totalrecall)
d
totalaccuracy = []
for i in models:
accuracies=i.accuracy(valid=True)[0][1]
totalaccuracy.append(accuracies)
e=statistics.mean(totalaccuracy)
e
f1 = a
threshold = b
precision = c
sensitivity = d
accuracy = e
metrics = {'Threshold': threshold, 'F1': f1, 'Precision': precision, 'Sensitivity': sensitivity, 'Accuracy': accuracy}
glm_3vars_performance = pd.DataFrame(metrics.values(), columns=['Value'], index=metrics.keys())
glm_3vars_performance
| Value | |
|---|---|
| Threshold | 8.66% |
| F1 | 44.87% |
| Precision | 40.41% |
| Sensitivity | 50.43% |
| Accuracy | 92.55% |
performances = [glm_performance, gbm_performance, glm_3vars_performance]
model_performances = pd.concat(performances, axis=1)
model_performances
| GLM | GBM | GLM 3 Variables | |
|---|---|---|---|
| Threshold | 0.160992 | 0.202369 | 0.086553 |
| F1 | 0.439394 | 0.410646 | 0.448669 |
| Precision | 0.394558 | 0.369863 | 0.404110 |
| Sensitivity | 0.495726 | 0.461538 | 0.504274 |
| Accuracy | 0.923986 | 0.920390 | 0.925526 |
GLM 3 Variables , GLM 3 Variables manage to build a much faster model than GBM.
filepath = r'~/Desktop/Desktop/Prasmul/Semester 6/Applied Data Science for Business/Project Group Dataset/'
filename = r'cctx_test.xlsx'
datatest = pd.read_excel(filepath + filename)
datatest.head()
| Transaction_ID | Transaction_Flag | Transaction_Date | Transaction_Type | Transaction_Amount | Bank_ID | CC_ID | Card_Type | Card_Holder | Channel_ID | Merchant_ID | Country_ID | City_ID | EDC_Type | EDC_Location | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | False | 2018-01-01 01:48:50.951 | T13 | 250000 | 1 | CCID6063 | CC12 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC3501 | LEDC3933 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | False | 2018-01-01 01:48:50.951 | T01 | 50000 | 1 | CCID5738 | CC10 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC2427 | LEDC1692 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | False | 2018-01-02 12:05:24.232 | T15 | 1000000 | 1 | CCID4708 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-013 | EDC1912 | LEDC1223 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | False | 2018-01-02 12:49:24.403 | T06 | 1200000 | 1 | CCID7183 | CC10 | 1 | 5 | M0001 | CTY06 | CTY06-073 | EDC0002 | LEDC3986 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | False | 2018-01-02 15:22:15.525 | T15 | 1000000 | 1 | CCID1248 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-181 | EDC3485 | LEDC2427 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
datatest.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2625 entries, 0 to 2624 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transaction_ID 2625 non-null object 1 Transaction_Flag 2625 non-null bool 2 Transaction_Date 2625 non-null datetime64[ns] 3 Transaction_Type 2625 non-null object 4 Transaction_Amount 2625 non-null int64 5 Bank_ID 2625 non-null int64 6 CC_ID 2625 non-null object 7 Card_Type 2625 non-null object 8 Card_Holder 2625 non-null int64 9 Channel_ID 2625 non-null int64 10 Merchant_ID 2625 non-null object 11 Country_ID 2625 non-null object 12 City_ID 2625 non-null object 13 EDC_Type 2625 non-null object 14 EDC_Location 2625 non-null object 15 EDC_Owner 2625 non-null object 16 Average_Transaction_Amount 2620 non-null float64 17 Maximum_Transaction_Amount 2620 non-null float64 18 Minimum_Transaction_Amount 2620 non-null float64 19 Average_Transaction_Frequency 2620 non-null float64 dtypes: bool(1), datetime64[ns](1), float64(4), int64(4), object(10) memory usage: 1.8 MB
def null_counts(df, style=True):
nulls = df.isna().sum().rename_axis('Columns').reset_index(name='Count')
nulls['Percentage'] = nulls['Count'] / len(df)
nulls = nulls.loc[nulls['Count'] > 0]
nulls.sort_values(by='Count', ascending=False, inplace=True)
nulls.reset_index(drop=True, inplace=True)
if style:
nulls = nulls.style.format({'Count': '{:,}', 'Percentage': '{:.2%}'}).hide_index()
return nulls
null_counts(datatest)
| Columns | Count | Percentage |
|---|---|---|
| Average_Transaction_Amount | 5 | 0.19% |
| Maximum_Transaction_Amount | 5 | 0.19% |
| Minimum_Transaction_Amount | 5 | 0.19% |
| Average_Transaction_Frequency | 5 | 0.19% |
datatest.dropna()
| Transaction_ID | Transaction_Flag | Transaction_Date | Transaction_Type | Transaction_Amount | Bank_ID | CC_ID | Card_Type | Card_Holder | Channel_ID | Merchant_ID | Country_ID | City_ID | EDC_Type | EDC_Location | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | False | 2018-01-01 01:48:50.951 | T13 | 250000 | 1 | CCID6063 | CC12 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC3501 | LEDC3933 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | False | 2018-01-01 01:48:50.951 | T01 | 50000 | 1 | CCID5738 | CC10 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC2427 | LEDC1692 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | False | 2018-01-02 12:05:24.232 | T15 | 1000000 | 1 | CCID4708 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-013 | EDC1912 | LEDC1223 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | False | 2018-01-02 12:49:24.403 | T06 | 1200000 | 1 | CCID7183 | CC10 | 1 | 5 | M0001 | CTY06 | CTY06-073 | EDC0002 | LEDC3986 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | False | 2018-01-02 15:22:15.525 | T15 | 1000000 | 1 | CCID1248 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-181 | EDC3485 | LEDC2427 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | False | 2018-12-31 10:18:30.622 | T03 | 1500000 | 1 | CCID4922 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC1055 | LEDC0293 | OEDC0377 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | False | 2018-12-31 10:32:42.888 | T15 | 100000 | 1 | CCID1508 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-133 | EDC2515 | LEDC1739 | OEDC0377 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | False | 2018-12-31 13:49:12.566 | T15 | 1100000 | 1 | CCID5731 | CC11 | 2 | 1 | M0001 | CTY06 | CTY06-171 | EDC1653 | LEDC0925 | OEDC0377 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | False | 2018-12-31 15:47:34.782 | T13 | 2050000 | 1 | CCID6497 | CC08 | 2 | 1 | M0001 | CTY06 | CTY06-023 | EDC0494 | LEDC3750 | OEDC0377 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | False | 2018-12-31 15:47:34.782 | T13 | 1000000 | 1 | CCID5547 | CC09 | 2 | 1 | M0001 | CTY06 | CTY06-023 | EDC0738 | LEDC3806 | OEDC0377 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2620 rows × 20 columns
datatest.describe()
| Transaction_Amount | Bank_ID | Card_Holder | Channel_ID | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|
| count | 2.625000e+03 | 2625.0 | 2625.000000 | 2625.000000 | 2.620000e+03 | 2.620000e+03 | 2.620000e+03 | 2620.000000 |
| mean | 1.294459e+06 | 1.0 | 1.932571 | 1.400000 | 1.354954e+06 | 1.227272e+07 | 7.315596e+04 | 2.437034 |
| std | 2.910836e+06 | 0.0 | 0.250811 | 1.012122 | 1.482863e+06 | 1.666874e+07 | 2.006759e+05 | 1.385924 |
| min | 8.000000e+03 | 1.0 | 1.000000 | 1.000000 | 5.000000e+04 | 3.800000e+04 | 1.000000e+00 | 1.000000 |
| 25% | 2.000000e+05 | 1.0 | 2.000000 | 1.000000 | 5.488456e+05 | 2.500000e+06 | 2.480000e+04 | 1.680000 |
| 50% | 5.500000e+05 | 1.0 | 2.000000 | 1.000000 | 1.013092e+06 | 6.000000e+06 | 3.505000e+04 | 2.110000 |
| 75% | 1.340900e+06 | 1.0 | 2.000000 | 1.000000 | 1.667487e+06 | 1.400000e+07 | 5.670075e+04 | 2.820000 |
| max | 7.500000e+07 | 1.0 | 2.000000 | 5.000000 | 1.963599e+07 | 1.000000e+08 | 5.700000e+06 | 19.780000 |
datatest.describe(include=['O'])
| Transaction_ID | Transaction_Type | CC_ID | Card_Type | Merchant_ID | Country_ID | City_ID | EDC_Type | EDC_Location | EDC_Owner | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 2625 | 2625 | 2625 | 2625 | 2625 | 2625 | 2625 | 2625 | 2625 | 2625 |
| unique | 2625 | 20 | 2404 | 13 | 329 | 7 | 133 | 1900 | 2063 | 472 |
| top | TX02656 | T01 | CCID4876 | CC11 | M0001 | CTY06 | CTY06-023 | EDC0002 | LEDC3506 | OEDC0377 |
| freq | 1 | 703 | 3 | 1005 | 2270 | 2617 | 1149 | 176 | 9 | 2093 |
datatest = datatest.drop(['Transaction_Flag','Country_ID','Bank_ID','CC_ID','Transaction_Date','EDC_Location'], axis=1)
datatest.head()
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | T13 | 250000 | CC12 | 2 | 1 | M0001 | CTY06-133 | EDC3501 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | T01 | 50000 | CC10 | 2 | 1 | M0001 | CTY06-133 | EDC2427 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | T15 | 1000000 | CC11 | 2 | 1 | M0001 | CTY06-013 | EDC1912 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | T06 | 1200000 | CC10 | 1 | 5 | M0001 | CTY06-073 | EDC0002 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | T15 | 1000000 | CC08 | 2 | 1 | M0001 | CTY06-181 | EDC3485 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
datatest['Card_Type'].replace({'CC11': 0, 'CC08': 1, 'CC09':2, 'CC00':3, 'CC10': 4}, inplace=True)
unused = datatest['Card_Type'].loc[(datatest['Card_Type']!= 0)&(datatest['Card_Type']!=1)& (datatest['Card_Type']!=2) & (datatest['Card_Type']!=3) &(datatest['Card_Type']!=4)]
datatest.replace(unused.values, 5, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | T13 | 250000 | 5 | 2 | 1 | M0001 | CTY06-133 | EDC3501 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | T01 | 50000 | 4 | 2 | 1 | M0001 | CTY06-133 | EDC2427 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | T15 | 1000000 | 0 | 2 | 1 | M0001 | CTY06-013 | EDC1912 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | T06 | 1200000 | 4 | 1 | 5 | M0001 | CTY06-073 | EDC0002 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | T15 | 1000000 | 1 | 2 | 1 | M0001 | CTY06-181 | EDC3485 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | T03 | 1500000 | 1 | 2 | 1 | M0001 | CTY06-133 | EDC1055 | OEDC0377 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | T15 | 100000 | 0 | 2 | 1 | M0001 | CTY06-133 | EDC2515 | OEDC0377 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | T15 | 1100000 | 0 | 2 | 1 | M0001 | CTY06-171 | EDC1653 | OEDC0377 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | T13 | 2050000 | 1 | 2 | 1 | M0001 | CTY06-023 | EDC0494 | OEDC0377 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | T13 | 1000000 | 2 | 2 | 1 | M0001 | CTY06-023 | EDC0738 | OEDC0377 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2625 rows × 14 columns
datatest['Transaction_Type'].replace({'T01': 0, 'T15': 1, 'T02':2, 'T08':3}, inplace=True)
unused = datatest['Transaction_Type'].loc[(datatest['Transaction_Type']!= 0)&(datatest['Transaction_Type']!=1)& (datatest['Transaction_Type']!=2) & (datatest['Transaction_Type']!=3)]
datatest.replace(unused.values, 4, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | 4 | 250000 | 5 | 2 | 1 | M0001 | CTY06-133 | EDC3501 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | 0 | 50000 | 4 | 2 | 1 | M0001 | CTY06-133 | EDC2427 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | 1 | 1000000 | 0 | 2 | 1 | M0001 | CTY06-013 | EDC1912 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | 4 | 1200000 | 4 | 1 | 5 | M0001 | CTY06-073 | EDC0002 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | 1 | 1000000 | 1 | 2 | 1 | M0001 | CTY06-181 | EDC3485 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | 4 | 1500000 | 1 | 2 | 1 | M0001 | CTY06-133 | EDC1055 | OEDC0377 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | 1 | 100000 | 0 | 2 | 1 | M0001 | CTY06-133 | EDC2515 | OEDC0377 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | 1 | 1100000 | 0 | 2 | 1 | M0001 | CTY06-171 | EDC1653 | OEDC0377 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | 4 | 2050000 | 1 | 2 | 1 | M0001 | CTY06-023 | EDC0494 | OEDC0377 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | 4 | 1000000 | 2 | 2 | 1 | M0001 | CTY06-023 | EDC0738 | OEDC0377 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2625 rows × 14 columns
datatest['Card_Holder'].replace({1: 0, 2: 1}, inplace=True)
datatest['EDC_Type'].replace({'EDC0002':0}, inplace=True)
unused = datatest['EDC_Type'].loc[datatest['EDC_Type']!= 0]
datatest.replace(unused.values, 1, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | 4 | 250000 | 5 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | 0 | 50000 | 4 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | 1 | 1000000 | 0 | 1 | 1 | M0001 | CTY06-013 | 1 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | 4 | 1200000 | 4 | 0 | 5 | M0001 | CTY06-073 | 0 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | 1 | 1000000 | 1 | 1 | 1 | M0001 | CTY06-181 | 1 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | 4 | 1500000 | 1 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | 1 | 100000 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | 1 | 1100000 | 0 | 1 | 1 | M0001 | CTY06-171 | 1 | OEDC0377 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | 4 | 2050000 | 1 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | 4 | 1000000 | 2 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2625 rows × 14 columns
datatest['Channel_ID'].replace({1:1}, inplace=True)
unused = datatest['Channel_ID'].loc[datatest['Channel_ID']!= 1]
datatest.replace(unused.values, 0, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | 0 | 250000 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | 0 | 50000 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | 1 | 1000000 | 0 | 1 | 1 | M0001 | CTY06-013 | 1 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | 0 | 1200000 | 0 | 0 | 0 | M0001 | CTY06-073 | 0 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | 1 | 1000000 | 1 | 1 | 1 | M0001 | CTY06-181 | 1 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | 0 | 1500000 | 1 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | 1 | 100000 | 0 | 1 | 1 | M0001 | CTY06-133 | 1 | OEDC0377 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | 1 | 1100000 | 0 | 1 | 1 | M0001 | CTY06-171 | 1 | OEDC0377 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | 0 | 2050000 | 1 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | 0 | 1000000 | 0 | 1 | 1 | M0001 | CTY06-023 | 1 | OEDC0377 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2625 rows × 14 columns
datatest['Merchant_ID'].replace({'M0001':1}, inplace=True)
unused = datatest['Merchant_ID'].loc[datatest['Merchant_ID']!= 1]
datatest.replace(unused.values, 0, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | 0 | 250000 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | 0 | 50000 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | 1 | 1000000 | 0 | 1 | 1 | 1 | CTY06-013 | 1 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | 0 | 1200000 | 0 | 0 | 0 | 1 | CTY06-073 | 0 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | 1 | 1000000 | 1 | 1 | 1 | 1 | CTY06-181 | 1 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | 0 | 1500000 | 1 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | 1 | 100000 | 0 | 1 | 1 | 1 | CTY06-133 | 1 | OEDC0377 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | 1 | 1100000 | 0 | 1 | 1 | 1 | CTY06-171 | 1 | OEDC0377 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | 0 | 2050000 | 1 | 1 | 1 | 1 | CTY06-023 | 1 | OEDC0377 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | 0 | 1000000 | 0 | 1 | 1 | 1 | CTY06-023 | 1 | OEDC0377 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2625 rows × 14 columns
datatest['City_ID'].replace({'CTY06-023':0}, inplace=True)
unused = datatest['City_ID'].loc[datatest['City_ID']!= 0]
datatest.replace(unused.values, 1, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | 0 | 250000 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | 0 | 50000 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | 1 | 1000000 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | 0 | 1200000 | 0 | 0 | 0 | 1 | 1 | 0 | OEDC0638 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | 1 | 1000000 | 1 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | 0 | 1500000 | 1 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | 1 | 100000 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | 1 | 1100000 | 0 | 1 | 1 | 1 | 1 | 1 | OEDC0377 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | 0 | 2050000 | 1 | 1 | 1 | 1 | 0 | 1 | OEDC0377 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | 0 | 1000000 | 0 | 1 | 1 | 1 | 0 | 1 | OEDC0377 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2625 rows × 14 columns
datatest['EDC_Owner'].replace({'OEDC0377':1}, inplace=True)
unused = datatest['EDC_Owner'].loc[datatest['EDC_Owner']!= 1]
datatest.replace(unused.values, 0, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | 0 | 250000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00003 | 0 | 50000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 102718.18 | 500000.0 | 50000.0 | 1.47 |
| 2 | TX00011 | 1 | 1000000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 3 | TX00012 | 0 | 1200000 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 4 | TX00013 | 1 | 1000000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2620 | TX13111 | 0 | 1500000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2621 | TX13112 | 1 | 100000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 857803.65 | 4200000.0 | 100000.0 | 2.18 |
| 2622 | TX13115 | 1 | 1100000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2623 | TX13119 | 0 | 2050000 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2624 | TX13120 | 0 | 1000000 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2625 rows × 14 columns
datatest.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2625 entries, 0 to 2624 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transaction_ID 2625 non-null object 1 Transaction_Type 2625 non-null int64 2 Transaction_Amount 2625 non-null int64 3 Card_Type 2625 non-null int64 4 Card_Holder 2625 non-null int64 5 Channel_ID 2625 non-null int64 6 Merchant_ID 2625 non-null int64 7 City_ID 2625 non-null int64 8 EDC_Type 2625 non-null int64 9 EDC_Owner 2625 non-null int64 10 Average_Transaction_Amount 2620 non-null float64 11 Maximum_Transaction_Amount 2620 non-null float64 12 Minimum_Transaction_Amount 2620 non-null float64 13 Average_Transaction_Frequency 2620 non-null float64 dtypes: float64(4), int64(9), object(1) memory usage: 287.2+ KB
datatest.reset_index(drop=True, inplace=True)
datatest
| Transaction_ID | Transaction_Type | Transaction_Amount | Card_Type | Card_Holder | Channel_ID | Merchant_ID | City_ID | EDC_Type | EDC_Owner | Average_Transaction_Amount | Maximum_Transaction_Amount | Minimum_Transaction_Amount | Average_Transaction_Frequency | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TX00001 | 0 | 250000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 444720.76 | 2300000.0 | 28900.0 | 1.53 |
| 1 | TX00011 | 1 | 1000000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1388235.29 | 7000000.0 | 30000.0 | 2.60 |
| 2 | TX00012 | 0 | 1200000 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1361486.42 | 5000000.0 | 26500.0 | 2.56 |
| 3 | TX00013 | 1 | 1000000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4143191.19 | 99000000.0 | 25000.0 | 3.05 |
| 4 | TX00017 | 0 | 96000 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1306697.23 | 7480000.0 | 25000.0 | 1.92 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2418 | TX13106 | 0 | 2150000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 687550.79 | 15000000.0 | 10000.0 | 2.61 |
| 2419 | TX13111 | 0 | 1500000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 895194.18 | 10000000.0 | 47500.0 | 1.90 |
| 2420 | TX13115 | 1 | 1100000 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 904045.87 | 9500000.0 | 20800.0 | 2.57 |
| 2421 | TX13119 | 0 | 2050000 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 4906702.85 | 75000000.0 | 100000.0 | 2.91 |
| 2422 | TX13120 | 0 | 1000000 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 617486.59 | 3000000.0 | 100000.0 | 1.67 |
2423 rows × 14 columns
datafinal = h2o.H2OFrame(datatest)
Parse progress: |█████████████████████████████████████████████████████████| 100%
predict = glm_3vars.predict(datafinal)
glm prediction progress: |████████████████████████████████████████████████| 100%
data_glm = h2o.as_list(predict, use_pandas=True)
data_glm
| predict | p0 | p1 | |
|---|---|---|---|
| 0 | 0 | 0.957768 | 0.042232 |
| 1 | 0 | 0.965820 | 0.034180 |
| 2 | 1 | 0.648173 | 0.351827 |
| 3 | 0 | 0.969390 | 0.030610 |
| 4 | 0 | 0.961974 | 0.038026 |
| ... | ... | ... | ... |
| 2418 | 0 | 0.963225 | 0.036775 |
| 2419 | 0 | 0.958111 | 0.041889 |
| 2420 | 0 | 0.965341 | 0.034659 |
| 2421 | 0 | 0.966049 | 0.033951 |
| 2422 | 0 | 0.957130 | 0.042870 |
2423 rows × 3 columns
datatest['Fraud_Status']=data_glm['predict']
final_prediction = datatest[['Transaction_ID', 'Fraud_Status']]
final_prediction
| Transaction_ID | Fraud_Status | |
|---|---|---|
| 0 | TX00001 | 0 |
| 1 | TX00011 | 0 |
| 2 | TX00012 | 1 |
| 3 | TX00013 | 0 |
| 4 | TX00017 | 0 |
| ... | ... | ... |
| 2418 | TX13106 | 0 |
| 2419 | TX13111 | 0 |
| 2420 | TX13115 | 0 |
| 2421 | TX13119 | 0 |
| 2422 | TX13120 | 0 |
2423 rows × 2 columns
final_prediction.to_csv('final_prediction.csv')